2006 Lecture Notes on
Hilbert Spaces and Quantum Mechanics
Draft: December 22, 2006

N.P. Landsman
Institute for Mathematics, Astrophysics, and Particle Physics
Radboud University Nijmegen
Toernooiveld 16525 ED NIJMEGENTHE NETHERLANDS
email: landsman@math.ru.nl
website: http://www.math.ru.nl/∼landsman/HSQM.html
tel.: 024-3652874
office: HG03.078

2Chapter I

Historical notes and overview
I.1

Introduction

The concept of a Hilbert space is seemingly technical and special. For example, the reader has
probably heard of the space ℓ2 (or, more precisely, ℓ2 (Z)) of square-summable sequences of realor complex numbers.1 That is, ℓ2 consists of all infinite sequences {. . . , c−2 , c−1 , c0 , c1 , c2 , . . .},ck ∈ K, for which∞X|ck |2 < ∞.
k=−∞Another example of a Hilbert space one might have seen is the space L2 (R) of square-integrable
complex-valued functions on R, that is, of all functions2 f : R → K for whichZ ∞dx |f (x)|2 < ∞.
−∞In view of their special nature, it may therefore come as a surprise that Hilbert spaces playa central role in many areas of mathematics, notably in analysis, but also including (differential)
geometry, group theory, stochastics, and even number theory. In addition, the notion of a Hilbert
space provides the mathematical foundation of quantum mechanics. Indeed, the definition of a
Hilbert space was first given by von Neumann (rather than Hilbert!) in 1927 precisely for thelatter purpose. However, despite his exceptional brilliance, even von Neumann would probably
not have been able to do so without the preparatory work in pure mathematics by Hilbert andothers, which produced numerous constructions (like the ones mentioned above) that are now
regarded as examples of the abstract notion of a Hilbert space. It is quite remarkable how aparticular development within pure mathematics crossed one in theoretical physics in this way;
this crossing is reminiscent to the one leading to the calculus around 1670; see below. Today, themost spectacular new application of Hilbert space theory is given by Noncommutative Geometry[3], where the motivation from pure mathematics is merged with the physical input from quantum
mechanics. Consequently, this is an important field of research in pure mathematics as well as inmathematical physics.
In what follows, we shall separately trace the origins of the concept of a Hilbert space inmathematics and physics. As we shall see, Hilbert space theory is part of functional analysis,an area of mathematics that emerged between approximately 1880–1930. Functional analysis isalmost indistinguishable from what is sometimes called ‘abstract analysis’ or ‘modern analysis,’
1 In what follows, we mainly work over the reals in order to serve intuition, but many infinite-dimensional vector
spaces, especially Hilbert spaces, are defined over the complex numbers. Hence we will write our formulae in a waythat is correct also for C instead of R. Of course, for z ∈ R the expression |z|2 is just z 2 . We will occasionally use
the fancy letter K, for Körper, which in these notes stands for either K = R or K = C.
2 As we shall see, the elements of L2 (R) are, strictly speaking, not simply functions but equivalence classes of
Borel functions. For detailed information we recommend the course Maat en Integraal by Professor A. van Rooij,
which runs parallel to the present one.

3

4

CHAPTER I. HISTORICAL NOTES AND OVERVIEW

which marked a break with classical analysis. The latter involves, roughly speaking, the studyof properties of a single function, whereas the former deals with spaces of functions.3 One may
argue that classical analysis is tied to classical physics,4 whereas modern analysis is associated withquantum theory. Of course, both kinds of analysis were largely driven by intrinsic mathematicalarguments as well.5 The final establishment of functional analysis and Hilbert space theory around1930 was made possible by combining a concern for rigorous foundations with an interest in physicalapplications [2].

I.2

Origins in mathematics

Cf. [2, 4, 17, 22] for more information on the history of functional analysis and Hilbert spaces. The
key idea behind functional analysis is to look at functions as points in some infinite-dimensional
vector space. To appreciate the depth of this idea, it should be mentioned that the concept of afinite-dimensional vector space, today routinely taught to first-year students, only emerged in thework of Grassmann between 1844 and 1862 (to be picked up very slowly by other mathematiciansbecause of the obscurity of Grassmann’s writings), and that even the far less precise notion ofa ‘space’ (other than a subset of Rn ) was not really known before the work of Riemann around
1850. Indeed, Riemann not only conceived the idea of a manifold (albeit in embryonic form, to bemade rigorous only in the 20th century), whose points have a status comparable to points in Rn ,but also explicitly talked about spaces of functions (initially analytic ones, later also more generalones). However, Riemann’s spaces of functions were not equipped with the structure of a vector
space. In 1885 Weierstrass considered the distance between two functions (in the context of thecalculus of variations), and in 1897 Hadamard took the crucial step of connecting the set-theoretic
ideas of Cantor with the notion of a space of functions. Finally, in his PhD thesis of 1906, which
is often seen as a turning point in the development of functional analysis, Hadamard’s studentFréchet defined what is now called a metric space (i.e., a possibly infinite-dimensional vector spaceequipped with a metric, see below), and gave examples of such spaces whose points are functions.6
After 1914, the notion of a topological space due to Hausdorff led to further progress, eventuallyleading to the concept of a topological vector space, which contains all spaces mentioned below as
special cases.
To understand the idea of a space of functions, we first reconsider Rn as the space of all
functions f : {1, 2, . . . , n} → R, under the identification x1 = f (1), . . . , xn = f (n). Clearly, under
this identification the vector space operations in Rn just correspond to pointwise operations onfunctions (e.g., f + g is the function defined by (f + g)(k) := f (k) + g(k), etc.). Hence Rn is afunction space itself, consisting of functions defined on a finite set.
The given structure of Rn as a vector space may be enriched by defining the length kf k of avector f and the associated distance d(f, g) = kf − gk between two vectors f and g. In addition,
3 The modern concept of a function as a map f: [a, b] → R was only arrived at by Dirichlet as late as 1837,
following earlier work by notably Euler and Cauchy. But Newton already had an intuitive graps of this concept, atleast for one variable.
4 Classical analysis grew out of the calculus of Newton, which in turn had its roots in both geometry and physics.
(Some parts of the calculus were later rediscovered by Leibniz.) In the 17th century, geometry was a practicalmatter involving the calculation of lenths, areas, and volumes. This was generalized by Newton into the calculus of
integrals. Physics, or more precisely mechanics, on the other hand, had to do with velocities and accellerations andthe like. This was abstracted by Newton into differential calculus. These two steps formed one of the most brilliant
generalizations in the history of mathematics, crowned by Newton’s insight that the operations of integration anddifferentiation are inverse to each other, so that one may speak of a unified differential and integral calculus, orbriefly calculus. Attempts to extend the calculus to more than one variable and to make the ensuing machinery
mathematically rigorous in the modern sense of the word led to classical analysis as we know it today. (Newton usedtheorems and proofs as well, but his arguments would be called “heuristic” or “intuitive” in modern mathematics.)
5 The jump from classical to modern analysis was as discontinuous as the one from classical to quantum mechanics.
The following anecdote may serve to illustrate this. G.H. Hardy was one of the masters of classical analysis and oneof the most famous mathematicians altogether at the beginning of the 20th century. John von Neumann, one of thefounders of modern analysis, once gave a talk on this subject at Cambridge in Hardy’s presence. Hardy’s comment
was: “Obviously a very intelligent man. But was that mathematics? ”
6 Fréchet’s main example was C[a, b], seen as a metric space in the supremum-norm, i.e., d(f, g) = kf − gk with
kf k = sup{f (x) | x ∈ [a, b]}.

5

I.2. ORIGINS IN MATHEMATICS

the angle θ between f and g in Rn is defined. Lengths and angles can both be expressed throughthe usual inner productnXf (k)g(k)(I.1)(f, g) =k=1

through the relations

andkf k =

p(f, f )

(f, g) = kf kkgk cos θ.

(I.2)
(I.3)

In particular, one has a notion of orthogonality of vectors, stating that f and g are orthogonal
whenever (f, g) = 0, and an associated notion of orthogonality of subspaces:7 we say that V ⊂ Rn
and W ⊂ Rn are orthogonal if (f, g) = 0 for all f ∈ V and g ∈ W . This, in turn, enables one todefine the (orthogonal) projection of a vector on a subspace of Rn .8 Even the dimension n of Rn
may be recovered from the inner product as the cardinality of an arbitrary orthogonal basis.9
Now replace {1, 2, . . . , n} by an infinite set. In this case the corresponding space of functions will
obviously be infinite-dimensional in a suitable sense.10 The simplest example is N = {1, 2, . . . , },so that one may define R∞ as the space of all functions f : N → R, with the associated vector
space structure given by pointwise operations. However, although R∞ is well defined as a vector
space, it turns out to be impossible to define an inner product on it, or even a length or distance.
Indeed, defining∞Xf (k)g(k)(I.4)(f, g) =k=1

it is clear that the associated length kf k (still given by (I.2)) is infinite for most f . This is hardly
surprising, since there are no growth conditions on f at infinity. The solution is to simply restrict
R∞ to those functions with kf k < ∞. These functions by definition form the set ℓ2 (N), which iseasily seen to be a vector space. Moreover, it follows from the Cauchy–Schwarz inequality(f, g) ≤ kf kkgk

(I.5)

that the inner product is finite on ℓ2 (N). Consequently, the entire geometric structure of Rn in so far
as it relies on the notions of lengths and angles (including orthogonality and orthogonal projections)is available on ℓ2 (N). Running ahead of the precise definition, we say that Rn ∼= ℓ2 ({1, 2, . . . , n})2
is a finite-dimensional Hilbert space, whereas ℓ (N) is an infinite-dimensional one. Similarly, one
may define ℓ2 (Z) (or indeed ℓ2 (S) for any countable set S) as a Hilbert space in the obvious way.
From a modern perspective, ℓ2 (N) or ℓ2 (Z) are the simplest examples of infinite-dimensional
Hilbert spaces, but historically these were not the first to be found.11 The initial motivation forthe concept of a Hilbert space came from the analysis of integral equations12 of the typeZ b
dy K(x, y)f (y) = g(x),(I.6)
f (x) +a

7 A subspace of a vector space is by definition a linear subspace.
8 This is most easily done by picking a basis {e } of the particular subspace V . The projection pf of f onto V isiPthen given by pf = i (ei , f )ei .
9 This is the same as the cardinality of an arbitrary basis, as any basis can be replaced by an orthogonal one by
the Gram–Schmidt procedure.
10 The dimension of a vector space is defined as the cardinality of some basis. The notion of a basis is complicatedin general, because one has to distinguish between algebraic (or Hamel) and topological bases. Either way, thedimension of the spaces described below is infinite, though the cardinality of the infinity in question depends on
the type of basis. The notion of an algebraic basis is very rarely used in the context of Hilbert spaces (and moregenerally Banach spaces), since the ensuing dimension is either finite or uncountable. The dimension of the spaces
below with respect to a topological basis is countably infinite, and for a Hilbert space all possible cardinalities may
occur as a possible dimension. In that case one may restrict oneself to an orthogonal basis.
11 From the point of view of most mathematicians around 1900, a space like ℓ2 (N) would have been far to abstract
to merit consideration.
12 Integral equations were initially seen as reformulations of differential equations. For example, the differential
R
R
R
equation Df = g or f ′ (x) = g(x) for unknown f is solved by f = g or f (x) = 0x dy g(y) = 01 dy K(x, y)g(y) for
K(x, y) = θ(x − y) (where x ≤ 1), which is an integral equation for g.

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CHAPTER I. HISTORICAL NOTES AND OVERVIEW

where f , g, and K are continuous functions and f is unknown. Such equations were first studied
from a somewhat modern perspective by Volterra and Fredholm around 1900, but the main breakthrough came from the work of Hilbert between 1904–1910. In particular, Hilbert succeeded inrelating integral equations to an infinite-dimensional generalization of linear algebra by choosing
an orthonormal basis {ek } of continuous functions on [a, b] (such as ek (x) := exp(2πkix) on theinterval [0, 1]), and defining the (generalized) Fourier coefficents of f by fˆk := (ek , f ) with respect
to the inner product
Z b
(I.7)
(f, g) :=dx f (x)g(x).
aThe integral equation (I.6) is then transformed into an equation of the typeXfˆk =K̂kl fˆl = ĝl .

(I.8)

l

Hilbert then noted from the Parseval relation (already well known at the time from Fourier analysis
and more general expansions in eigenfunctions)Xk∈Z|fˆk |2 =Z b
adx |f (x)|2(I.9)

that the left-hand side is finite, so that fˆ ∈ ℓ2 (Z). This, then, led him and his students to study ℓ2
also abstractly. E. Schmidt should be mentioned here in particular. Unlike Hilbert, already in 1908
he looked at ℓ2 as a ‘space’ in the modern sense, thinking of seqences (ck ) as point in this space.
Schmidt studied the geometry of ℓ2 as a Hilbert space in the modern sense, that is, empasizing
the inner product, orthogonality, and projections, and decisively contributed to Hilbert’s work on
spectral theory.
The space L2 (a, b) appeared in 1907 in the work of F. Riesz13 and Fischer as the space of(Lebesgue) integrable functions14 on (a, b) for whichZ badx |f (x)|2 < ∞;of course, this condition holds if f is continuous on [a, b]. Equipped with the inner product (I.7), thiswas another early example of what is now called a Hilbert space.15 The context of its appearance
was what is now called the Riesz–Fischer theorem: Given any sequence (ck ) of real (or complex)
numbers and any orthonormal system (ek ) in P
L2 (a, b),16 there exists a function f ∈ L2 (a, b) for2
which (ek , f ) = ck if and only if c ∈ ℓ , i.e., if k |ck |2 < ∞.
At the time, the Riesz–Fischer theorem was completely unexpected, as it proved that two seemingly totally different spaces were ‘the same’ from the right point of view. In modern terminology,
the theorem establishes an isomorphism of ℓ2 and L2 as Hilbert spaces, but this point of view was
only established twenty years later, i.e., in 1927, by von Neumann. Inspired by quantum mechanics (see below), in that year von Neumann gave the definition of a Hilbert space as an abstract
mathematical structure, as follows. First, an inner product on a vector space V over a field K(where K = R or K = C), is a map V × V → K, written as hf, gi 7→ (f, g), satisfying, for all
f, g ∈ V and t ∈ K,
1. (f, f ) ≥ 0;
2. (g, f ) = (f, g);
13 Frederic Riesz had a brother, Marcel Riesz, who was a well-known mathematician too.
14 More precisely, the elements of L2 (a, b) are not functions but equivalence classes thereof, where f ∼ g whenkf − gk2 = 0.
15 The term ‘Hilbert space’ was first used by Schoenflies in 1908 for ℓ2 , and was introduced in the abstract senseby von Neumann in 1927; see below.
16 The notion of an orthonormal system of functions on the interval [a, b] was as old as Fourier, and was defined
abstractly by Hilbert in 1906.

7

I.2. ORIGINS IN MATHEMATICS

3. (f, tg) = t(f, g);
4. (f, g + h) = (f, g) + (f, h);
5. (f, f ) = 0 ⇒ f = 0.
Given an inner product on V , one defines an associated length function or norm (see below)
k · k : V → R+ by (I.2). A Hilbert space (over K) is a vector space (over K) with inner product,with the property that Cauchy sequences with respect to the given norm are convergent (in otherwords, V is complete in the given norm).17 Hilbert spaces are denoted by the letter H rather than
V . Thus Hilbert spaces preserve as much as possible of the geometry of Rn .
It can be shown that the spaces mentioned above are Hilbert spaces. Defining an isomorphismof Hilbert spaces U : H1 → H2 as an invertible linear map preserving the inner product (i.e.,(U f, U g)2 = (f, g)1 for all f, g ∈ H1 ), the Riesz–Fischer theorem shows that ℓ2 (Z) and L2 (a, b) areindeed isomorphic.
In a Hilbert space the inner product is fundamental, the norm being derived from it. However,one may instead take the norm as a starting point (or, even more generally, the metric, as done byFréchet in 1906). The abstract properties of a norm were first identified by Riesz in 1918 as being
satisfied by the supremum norm, and were axiomatized by Banach in his thesis in 1922. A norm
on a vector space V over a field K as above is a function k · k : V → R+ with the properties:
1. kf + gk ≤ kf k + kgk for all f, g ∈ V ;2. ktf k = |t|kf k; for all f ∈ V and t ∈ K;
3. kf k = 0 ⇒ f = 0.
The usual norm on Rn satisfies these axioms, but there are many other possibilities, such as
kf kp :=

n
X

k=1

p

|f (k)|!1/p(I.10)

for any p ∈ R with 1 ≤ p < ∞, or
kf k∞ := sup{|f (k)|, k = 1, . . . , n}.
In the finite-dimensional case, these norms (and indeed all other norms) are all equivalent in thesense that they lead to the same criterion of convergence (technically, they generate the same
topology): if we say that fn → f when kfn − f k → 0 for some norm on Rn , then this implies
convergence with respect to any other norm. This is no longer the case in infinite dimension. For
example, one may define ℓp (N) as the subspace of R∞ that consists of all vectors f ∈ R∞ for which
kf kp :=

∞
X

k=1

p

|f (k)|!1/p(I.11)

is finite. It can be shown that k · kp is indeed a norm on ℓp (N), and that this space is completein this norm. As with Hilbert spaces, the examples that originally motivated Riesz to give his
definition were not ℓp spaces but the far more general Lp spaces, which he began to study in 1910.
For example, Lp (a, b) consists of all (equivalence classes of Lebesgue) integrable functions f on(a, b) for which
!1/p
Z b
p
kf kp :=
dx |f (x)|(I.12)a17 A sequence (f ) is a Cauchy sequence in V when kf − f k → 0 when n, m → ∞; more precisely, for any ε > 0nnmthere is N ∈ N such that kfn − fm k < ε for all n, m > N . A sequence (fn ) converges if there is f ∈ V such thatlimn→∞ kfn − f k = 0.

8

CHAPTER I. HISTORICAL NOTES AND OVERVIEW

is finite, still for 1 ≤ p < ∞, and also kf k∞ := sup{|f (x)|, x ∈ (a, b)}. Eventually, in 1922 Banachdefined what is now called a Banach space as a vector space (over K as before) that is complete
in some given norm.
Long before the abstract definitions of a Hilbert space and a Banach space were given, people
began to study the infinite-dimensional generalization of functions on Rn . In the hands of Volterra,
the calculus of variations originally inspired the study of functions ϕ : V → K, later called functionals, and led to early ideas about possible continuity of such functions. However, although thecalculus of variations involved nonlinear functionals as well, only linear functionals turned out tobe tractable at the time (until the emergence of nonlinear functional analysis much later). Indeed,even today (continuous) linear functionals still form the main scalar-valued functions that arestudied on infinite-dimensional (topological) vector spaces. For this reason, throughout this text afunctional will denote a continuous linear functional. For H = L2 (a, b), it was independently
proved by Riesz and Fréchet in 1907 that any functional on H is of the form g 7→ (f, g) for somef ∈ H.18 The same result for arbitrary Hilbert spaces H was written down only in 1934–35, again
by Riesz, although it is not very difficult.
The second class of ‘functions’ on Hilbert spaces and Banach spaces that could be analyzed
in detail were the generalizations of matrices on Rn , that is, linear maps from the given space toitself. Such functions are now called operators.19 For example, the integral equation (I.6) is thensimply of the form (1 + K)f = g, where 1 : L2 (a, b) → L2 (a, b) is the identity operator 1f = f ,Rb
and K : L2 (a, b) → L2 (a, b) is the operator given by (Kf )(x) = a dy K(x, y)f (y). This is easy
for us to write down, but in fact it took some time before integral of differential equations wereinterpreted in terms of operators acting on functions.20 They managed to generalize practicallyall results of linear algebra to operators, notably the existence of a complete set of eigenvectors for
operators of the stated type with symmetric kernel, that is, K(x, y) = K(y, x).21
The abstract concept of a (bounded) operator (between what we now call Banach spaces) is due
to Riesz in 1913. It turned out that Hilbert and Schmidt had studied a special class of operators
we now call ‘compact’, whereas an even more famous student of Hilbert’s, Weyl, had investigated asingular class of operators now called ’unbounded’ in the context of ordinary differential equations.
Spectral theory and eigenfunctions expansions were studied by Riesz himself for general bounded
operators on Hilbert spaces (seen by him as a special case of general normed spaces), and later,
more specifically in the Hilbert space case, by Hellinger and Toeplitz (culminating in their pre-von
Neumann review article of 1927).
In the Hilbert space case, the results of all these authors were generalized almost beyond
recognition by von Neumann in his book from 1932 [15], to whose origins we now turn.

I.3

Origins in physics

For more details on the origin of the Hilbert space concept in physics see [9, 11, 14]. For biographicalinformation on John von Neumann22 see [6, 7, 13, 25].
From 1900 onwards, physicists had begun to recognize that the classical physics of Newton,
Maxwell and Lorentz (i.e., classical mechanics, Newtonian gravity, and electrodynamics) could notdescribe all of Nature. The fascinating era that was thus initiated by Planck, to be continued
18 More generally, in 1910 Riesz showed that any functional on Lp (a, b) is given by an element Lq (a, b), where
1/p + 1/q = 1, by the same formula. Since p = 2 implies q = 2, this of course implies the earlier Hilbert space result.
19 Or linear operators, but for us linearity is part of the definition of an operator.
20 For example, Hilbert and Schmidt did not really have the operator concept but (from the modern point of view)
worked in terms of the associated quadratic form. That is, the operator a : H → H defines a map q : H × H → K
by hf, gi 7→ (f, ag).
21 The associated quadratic form then satisfies q(f, g) = q(g, f ).
22 John von Neumann (1903–1957) was a Hungarian prodigy; he wrote his first mathematical paper at the age ofseventeen. Except for this first paper, his early work was in set theory and the foundations of mathematics. In theFall of 1926, he moved to Göttingen to work with Hilbert, the most prominent mathematician of his time. Around
1920, Hilbert had initiated his Beweistheory, an approach to the axiomatization of mathematics that was doomed
to fail in view of Gödel’s later work. However, at the time that von Neumann arrived, Hilbert was mainly interested
in quantum mechanics; see below.

I.3. ORIGINS IN PHYSICS

9mainly by Einstein, Bohr, and De Broglie, ended in 1925–1927 with the discovery of quantum
mechanics. This theory replaced classical mechanics, and was initially discovered in two guises.
First, Heisenberg discovered a form of quantum mechanics that at the time was called ‘matrix
mechanics.’ Heisenberg’s basic idea was that in atomic physics physical observables (that is,measurable quantities) should not depend on continuous variables like position and momentum(as he did not believe the concept of an electronic orbit in an atom made sense), but on discrete
quantities, like the natural numbers n = 1, 2, 3, . . . labelling the orbits in Bohr’s model of the atom.
Specifically, Heisenberg thought that in analogy to a ‘quantum jump’ from one orbit to the other,everything should be expressed in terms of two such numbers. Thus he replaced the functions
f (x, p) of position and momentum in terms of which classical physics is formulated by quantities
f (m, n). In order to secure the law of conservationPof energy in his new mechanics, he was forced
to postulate the multiplication rule f ∗ g(m, n) = l f (m, l)g(l, n), replacing the rule f g(x, p) =f (x, p)g(x, p) of classical mechanics. He noted that f ∗ g 6= g ∗ f , unlike in classical mechanics, andsaw in this non-commutativity of physical observables the key revolutionary character of quantum
mechanics. When he showed his work to his boss Born, a physicist who as a former assistantto Hilbert was well versed in mathematics, Born saw, after a sleepless night, that Heisenberg’s
multiplication rule was the same as the one known for matrices, but now of infinite size.23 Thus
Heisenberg’s embryonic formulation of quantum theory, written down in 1925 in a paper generally
seen as the birth of quantum mechanics, came to be known as ‘matrix mechanics’.
Second, Schrödinger was led to a formulation of quantum theory called ‘wave mechanics’, inwhich the famous symbol Ψ, denoting a ‘wave function,’ played an important role. To summarize along story, Schrödinger based his work on de Broglie’s idea that in quantum theory a wave should be
associated to each particle; this turned Einsteins’s concept of a photon from 1905 on its head.24 De
Broglie’s waves should, of course, satisfy some equation, similar to the fundamental wave equationor Maxwell’s equations. It is this equation that Schrödinger proposed in 1926 and which is now
named after him.25 Schrödinger found his equation by studying the transition from wave optics togeometric optics, and by (wrongly) believing that there should be a similar transition from wave
mechanics to classical mechanics.26
Thus in 1926 one had two alternative formulations of quantum mechanics, which looked completely different, but each of which could explain certain atomic phenomena. The relationship andpossible equivalence between these formulations was, of course, much discussed at the time. The
most obvious difficulty in relating Heisenberg’s work to Schrödinger’s was that the former was atheory of observables lacking the concept of a state, whereas the latter had precisely the opposite
feature: Schrödinger’s wave functions were states, but where were the observables? To answer thisquestion, Schrödinger introduced his famous expressions Q = x (more precisely, QΨ(x) = xΨ(x))
and P = −i~∂/∂x, defining what we now call unbounded operators on the Hilbert space L2 (R3 ).
Subsequently, Dirac, Pauli, and Schrödinger himself recognized that wave mechanics was related tomatrix mechanics in the following way: Heisenberg’s matrix x(m, n) was nothing but the matrix
element (en , Qem ) of the position operator Q with respect to the orthonormal basis of L2 (R3 )given by the eigenfunctions of the Hamiltonian H = P 2 /2m + V (Q). Conversely, the vectors in ℓ2
on which Heisenberg’s matrices acted could be interpreted as states. However, these observations
fell far short of an equivalence proof of wave mechanics and matrix mechanics (as is sometimes
claimed), let alone of a mathematical understanding of quantum mechanics.
Heisenberg’s paper was followed by the ‘Dreimännerarbeit’ of Born, Heisenberg, and Jordan
23 At the time, matrices and linear algebra were unknown to practically all physicists.
24 Einstein’s revolutionary proposal, which marked the true conceptual beginning of quantum theory, had beenthat light, universally seen as a wave phenomenon at the time, had a particle nature as well. The idea that lightconsists of particles had earlier been proposed by none other than Newton, but had been discredited after the
discovery of Young around 1800 (and its further elaboration by Fresnel) that light displayes interference phenomena
and therefore should have a wave nature. This was subsequently confirmed by Maxwell’s theory, in which light is an
oscillation of the electromagnetic field. In his PhD thesis from 1924, de Broglie generalized and inverted Einstein’s
reasoning: where the latter had proposed that light waves are particles, the former postulated that particles arewaves.
25 He first found the time-independent Schrödinger equation HΨ = EΨ with H = −~2 ∆/2m+V , and subsequently
got the time-dependent one HΨ = i~∂Ψ/∂t.
26 Technically, Schrödinger relied on the Hamilton–Jacobi formulation of classical mechanics.

10

CHAPTER I. HISTORICAL NOTES AND OVERVIEW(1926); all three were in Göttingen at the time. Born turned to his former teacher Hilbert formathematical advice. Hilbert had been interested in the mathematical structure of physical theories
for a long time; his Sixth Problem (1900) called for the mathematical axiomatization of physics.
Aided by his assistants Nordheim and von Neumann, Hilbert ran a seminar on the mathematicalstructure of quantum mechanics, and the three wrote a joint paper on the subject (now obsolete).
It was von Neumann alone who, at the age of 23, recognized the mathematical structure ofquantum mechanics. In this process, he defined the abstract concept of a Hilbert space discussed
above; as we have said, previously only some examples of Hilbert spaces had been known. Von
Neumann saw that Schrödinger’s wave functions were unit vectors in the Hilbert space L2 (R3 ),and that Heisenberg’s observables were linear operators on the Hilbert space ℓ2 . The Riesz–Fischer
theorem then implied the mathematical equivalence between wave mechanics and matrix mechanics. In a series of papers that appeared between 1927–1929, von Neumann defined Hilbert space,formulated quantum mechanics in this language, and developed the spectral theory of bounded
as well as unbounded normal operators on a Hilbert space. This work culminated in his book[15], which to this day remains the definitive account of the mathematical structure of elementary
quantum mechanics.27
Von Neumann proposed the following mathematical formulation of quantum mechanics. The
observables of a given physical system are the self-adjoint (possibly unbounded) linear operators
a on a Hilbert space H. The pure states of the system are the unit vectors in H. The expectation
value of an observable a in a state ψ is given by (ψ, aψ). The transition probability between two
states ψ and ϕ is |(ψ, ϕ)|2 . As we see from (I.3), this number is just (cos θ)2 , where θ is theangle between the unit vectors ψ and ϕ. Thus the geometry of Hilbert space has a direct physicalinterpretation in quantum mechanics, surely one of von Neumann’s most brilliant insights. Lateron, he would go beyond his Hilbert space approach to quantum theory by developing such topics
and quantum logic (see [1]) and operator algebras (cf. [3]).

27 Von Neumann’s book was preceded by Dirac’s The Principles of Quantum Mechanics (1930), which contains
another brilliant, but this time mathematically questionable account of quantum mechanics in terms of linear spaces
and operators.

Chapter II

Metric spaces, normed spaces, andHilbert spaces
II.1

Basic definitions

We repeat two basic definitions from the Introduction, and add a third:
Definition II.1 Let V be a vector space over a field K (where K = R or K = C).
An inner product on V is a map V × V → K, written as hf, gi 7→ (f, g), satisfying, for all
f, g, h ∈ V and t ∈ K:
1. (f, f ) ∈ R+ := [0, ∞) (positivity);

2. (g, f ) = (f, g) (symmetry);
3. (f, tg) = t(f, g) (linearity 1);
4. (f, g + h) = (f, g) + (f, h) (linearity 2);
5. (f, f ) = 0 ⇒ f = 0 (positive definiteness).

A norm on V is a function k · k : V → R+ satisfying, for all f, g, h ∈ V and t ∈ K:

1. kf + gk ≤ kf k + kgk (triangle inequality);
2. ktf k = |t|kf k (homogeneity);
3. kf k = 0 ⇒ f = 0 (positive definiteness).

A metric on V is a function d : V × V → R+ satisfying, for all f, g, h ∈ V :1. d(f, g) ≤ d(f, h) + d(h, g) (triangle inequality);
2. d(f, g) = d(g, f ) for all f, g ∈ V (symmetry);
3. d(f, g) = 0 ⇔ f = g (definiteness).
The notion of a metric applies to any set, not necessarily to a vector space, like an inner productand a norm. These structures are related in the following way:1
1 Apart from a norm, an inner product defines another structure called a transition probability, which is ofgreat importance to quantum mechanics; cf. the Introduction. Abstractly, a transition probability on a set S isa function p : S × S → [0, 1] satisfying p(x, y) = 1 ⇔ x = y (cf. Property 3 of a metric) and p(x, y) = p(y, x).
See [10]. Now take the set S of all vectors in a complex inner product space that have norm 1, and define anequivalence relation on S by f ∼ g iff f = zg for some z ∈ C with |z| = 1. (Without taking equivalence classes
the first axiom would not be satisfied). The set S = S/ ∼ is then equipped with a transition probability definedby p([f ], [g]) := |(f, g)|2 . Here [f ] is the equivalence class of f with kf k = 1, etc. In quantum mechanics vectorsof norm 1 are (pure) states, so that the transition probability between two states is determined by their angle θ.
(Recall the elementary formula from Euclidean geometry (x, y) = kxkkyk cos θ, where θ is the angle between x andy in Rn .)

11

12

CHAPTER II. METRIC SPACES, NORMED SPACES, AND HILBERT SPACES

Proposition II.2

1. An inner product on V defines a norm on V by means of kf k =2. A norm on V defines a metric on V through d(f, g) := kf − gk.

p(f, f ).

The proof of this claim is an easy exercise; part 1 is based on the Cauchy–Schwarz inequality|(f, g)| ≤ kf kkgk,(II.1)

whose proof in itself is an exercise, and part 2 is really trivial: the three axioms on a norm
immediately imply the corresponding properties of the metric. The question arises when a norm
comes from an inner product in the stated way: this question is answered by the Jordan–von
Neumann theorem:p
Theorem II.3 A norm k·k on a vector space comes from an inner product through kf k = (f, f )if and only if
kf + gk2 + kf − gk2 = 2(kf k2 + kgk2 ).
(II.2)In that case, one has
4(f, g) = kf + gk2 − kf − gk2 for K = R
and
4(f, g) = kf + gk2 − kf − gk2 + ikf − igk2 − ikf + igk2 for K = C.
We leave the proof of this theorem as an exercise as well, though it is by no means trivial.
Applied to the ℓp and Lp spaces mentioned in the introduction, this yields the result that thenorm in these spaces comes from an inner product if and only if p = 2; see below for a precise
definition of Lp (Ω) for Ω ⊆ Rn . There is no (known) counterpart of this result for the transition
from a norm to a metric.2 It is very easy to find examples of metrics that do not come from a
norm: on any vector space (or indeed any set) V the formula d(f, g) = δf g defines a metric notderived from a norm. Also, if d is any metric on V , then d′ = d/(1 + d) is a metric, too: since
cleary d′ (f, g) ≤ 1 for all f, g, this metric can never come from a norm.

II.2

Convergence and completenessThe reason we look at metrics in a Hilbert space course is, apart from general education, thatmany concepts of importance for Hilbert spaces are associated with the metric rather than withthe underlying inner product or norm. The main such concept is convergence:
Definition II.4 Let (xn ) := {xn }n∈N be a sequence in a metric space (V, d). We say that xn → x(i.e., (xn ) converges to x ∈ V ) when limn→∞ d(xn , x) = 0, or, more precisely: for any ε > 0 there
is N ∈ N such that d(xn , x) < ε for all n > N .
In a normed space, hence in particular in a space with inner product, this therefore means thatlimn→∞ kxn − xk = 0.3
A sequence (xn ) in (V, d) is called a Cauchy sequence when d(xn , xm ) → 0 when n, m → ∞;more precisely: for any ε > 0 there is N ∈ N such that d(xn , xm ) < ε for all n, m > N . Clearly, a
convergent sequence is Cauchy: from the triangle inequality and symmetry one hasd(xn , xm ) ≤ d(xn , x) + d(xm , x).
So for given ε > 0 there is N ∈ N such that d(xn , x) < ε/2, etcetera. However, the converse
statement does not hold in general, as is clear from the example of the metric space (0, 1) withmetric d(x, y) = |x − y|: the sequence xn = 1/n does not converge in (0, 1) (for an example
involving a vector space see the exercises). In this case one can simply extend the given space to[0, 1], in which every Cauchy sequence does converge.
2 More generally, a metric (on an arbitrary set) defines a so-called topology on this set, but we leave this to the
Topology course by F. Clauwens.
3 Such convergence is sometimes called strong convergence, in contrast to weak convergence, which for an
inner product space means that limn |(y, xn − x)| = 0 for each y ∈ V .

13

II.2. CONVERGENCE AND COMPLETENESS

Definition II.5 A metric space (V, d) is called complete when every Cauchy sequence converges.
• A vector space with norm that is complete in the associated metric is called a Banach space.
• A vector space with inner product that is complete in the associated metric is called a Hilbert
space.
The last part may be summarized as follows: a vector space H with inner product ( , ) is a
Hilbert space when every sequence (xn ) such that lim
pn,m→∞ kxn − xm k = 0 has a limit x ∈ H in
the sense that limn→∞ kxn − xk = 0 (where kxk = (x, x)). It is easy to see that such a limit isunique.
Like any good definition, this one too comes with a theorem:˜ (unique up to
Theorem II.6 For any metric space (V, d) there is a complete metric space (Ṽ , d)isomorphism) containing (V, d) as a dense subspace4 on which d˜ = d. If V is a vector space, thenso is Ṽ . If the metric d comes from a norm, then Ṽ carries a norm inducing d˜ (so that Ṽ , being
complete, is a Banach space). If the norm on V comes from an inner product, then Ṽ carries aninner product, which induces the norm just mentioned (so that Ṽ is a Hilbert space), and whose
restriction to V is the given inner product.
Since this theorem is well known and basic in analysis (see [28], §9.5.2., pp. 24–27), we will notgive a complete proof, but will just sketch the main idea. In this course one only needs the casewhere the metric comes from a norm, so that d(xn , yn ) = kxn − yn k etc. in what follows.
One defines the completion Ṽ as the set of all Cauchy sequences (xn ) in V , modulo the equivalence relation (xn ) ∼ (yn ) when limn d(xn , yn ) = 0. (When xn and yn converge in V , this meansthat they are equivalent when they have the same limit.) The metric d˜ on the set of such equiva˜ n ], [yn ]) := limn d(xn , yn ).5 The embedding ι : V ֒→ Ṽlence classes [xn ] := [(xn )] is defined by d([xis given by identifying x ∈ V with the Cauchy sequence (xn = x ∀n), i.e., ι(x) = [xn = x]. It
follows that a Cauchy sequence (xn ) in V ⊆ Ṽ converges to [xn ], for˜˜lim d(ι(xm ), [xn ]) = lim d([xn = xm ], [xn ]) = lim lim d(xm , xn ) = 0
m

m

m

n

by definition of a Cauchy sequence. Furthermore, one can show that any Cauchy sequence in Ṽconverges by approximating its elements by elements of V .
If V is a vector space, the corresponding linear structure on Ṽ is given by [xn ]+[yn ] := [xn +yn ]and t[xn ] := [txn ]. If V has a norm, the corresponding norm on Ṽ is given by k[xn ]k := limn kxn k.6
If V has an inner product, the corresponding inner product on Ṽ is given by ([xn ], [yn ]) :=limn (xn , yn ).7
A finite-dimensional vector space is complete in any possible norm. In infinite dimension,
completeness generally depends on the norm (which often can be chosen in many different ways,
even apart from trivial operations like rescaling by a positive constant). For example, take V = ℓc ,the space of functions f : N → C (or, equivalently, of infinite sequence (f (1), f (2), . . . , f (k), . . .))with only finitely many f (k) 6= 0. Two interesting norms on this space are:4 This means that any point in Ṽkf k∞:= sup{|f (k)|};

kf k2

∞
X

(II.3)

k

:=

k=1

2

|f (k)|!1/2.

(II.4)

˜is the limit of some convergent sequence in V with respect to the metric d.
n , yn ) − d(xm , ym )| ≤ d(xn , xm ) +5 This limit exists, since using the triangle inequality one easily shows that |d(xd(yn , ym ).
6 Note that (kx k) is a Cauchy sequence in R (from the inequality kx k − kx k ≤ kx − x k, which you get
n
n
m
n
m
from the triangle inequality for the norm).
7 The existence of this limit is an easy exercise: we write (x , y ) − (x , y ) = (x − x , y ) + (x , y − y )
n n
m m
n
m n
m n
m
hence, from Cauchy-Schwarz, |(xn , yn ) − (xm , ym )| ≤ kxn − xm kkyn k + kyn − ym kkxm k. Since kyn k and kxm k are
Cauchy, hence convergent (see previous footnote), hence bounded, (xn , yn ) is a Cauchy sequence in C.

14

CHAPTER II. METRIC SPACES, NORMED SPACES, AND HILBERT SPACESℓc is not complete in either norm. However, the spaceℓ∞ := {f : N → C | kf k∞ < ∞}(II.5)

is complete in the norm k · k∞ , and the spaceℓ2 := {f : N → C | kf k2 < ∞}(II.6)

is complete in the norm k · k2 . In fact, ℓ2 is a Hilbert space in the inner product(f, g) :=∞X

f (k)g(k).

(II.7)

k=1

Now we seem to face a dilemma. One the one hand, there is the rather abstract completion
procedure for metric spaces just sketched: it looks terrible to use in practice. On the other hand,
we would like to regard ℓ∞ as the completion of ℓc in the norm k · k∞ and similarly we would liketo see ℓ2 as the completion of ℓc in the norm k · k2 .
This can indeed be done through the following steps, which we just outline for the Hilbert spacecase ℓ2 (similar comments hold for ℓ∞ and are left to the reader):1. Embed V = ℓc is some larger space W : in this case, W is the space of all sequences (or of
all functions f : N → C).
2. Guess a maximal subspace H of W in which the given norm k · k2 is finite: in this case thisis H = ℓ2 .
3. Prove that H is complete.
4. Prove that V is dense in H, in the sense that each element f ∈ H is the limit of a Cauchy
sequence in V .
The last step is usually quite easy. For example, any element f ∈ ℓ2 is the limit (with respect tothe norm k · k2 , of course), of the sequence (fn ) where fn (k) = f (k) if k ≤ n and fn (k) = 0 if
k > n. Clearly, fn ∈ ℓc for all n.
The third step may in itself be split up in the following way:
• Take a generic Cauchy sequence (fn ) in H and guess its limit f in W .
• Prove that f ∈ H.
• Prove that limn kfn − f k = 0.
Here also the last step is often easy, given the previous ones.
In our example this procedure is implemented as follows.
• If (fn ) is any sequence in ℓ2 , the the definition of the norm implies that for each k one has|fn (k) − fm (k)| ≤ kfn − fm k2 ,So if (fn ) is a Cauchy sequence in ℓ2 , then for each k, (fn (k)) is a Cauchy sequence in C.
Since C is complete, the latter has a limit called f (k). This defines f ∈ W as the candidate
limit of (fn ), simply by f : k 7→ f (k) := limn fn (k).
• For each n one has:kfn − f k22 =

∞
X

k=1

|fn (k) − f (k)|2 = lim

lim

N →∞ m→∞NX

k=1|fn (k) − fm (k)|2 .

By the definition of lim sup and using the positivity of all terms one has
lim

m→∞

N
X

k=1

|fn (k) − fm (k)|2 ≤ lim sup

m→∞

∞
X

k=1

|fn (k) − fm (k)|2 = lim sup kfn − fm k22 .
m→∞

15

II.2. CONVERGENCE AND COMPLETENESS

Hence
kfn − f k22 ≤ lim sup kfn − fm k22 .
m→∞Since (fn ) is Cauchy, this can be made < ǫ2 for n > N . Hence kfn − f k2 ≤ ǫ, so fn − f ∈ ℓ2
and since fn ∈ ℓ2 and ℓ2 is a vector space, it follows that f ∈ ℓ2 = H, as desired.
• The claim limn kfn − f k = 0 follows from the same argument.
Returning to our dilemma, we wish to establish a link between the “practical” completion ℓ2
of ℓc and the formal completion ℓ˜c . Such a link is given by the concept of isomorphism of two
Hilbert spaces H1 and H2 . As in the Introduction, we define an isomorphism of Hilbert spaces
U : H1 → H2 as an invertible linear map preserving the inner product (i.e., (U f, U g)2 = (f, g)1
for all f, g ∈ H1 ). Such a map U is called a unitary transformation and we write H1 ∼= H2 .
So, in order to identify ℓ2 with ℓ˜c we have to find such a U : ℓ˜c → ℓ2 . This is easy: if (fn ) is
Cauchy in ℓc we put
U ([fn ]) := f = lim fn ,(II.8)nwhere f is the limit as defined above. It is an exercise to check that:
1. This map is well-defined, in the sense that if fn ∼ gn then limn fn = limn gn ;2. This map is indeed invertible and preserves the inner product.
We now apply the same strategy to a more complicated situation. Let Ω ⊆ Rn be an open or
closed subset of Rn ; just think of Rn itself for the quantum theory of a particle, of [−π, π] ⊂ R for
Fourier analysis as done in Analysis 3 [28].8 The role of ℓc in the previous analysis is now played
by Cc (Ω), the vector space of complex-valued continuous functions on Ω with compact support.9
Again, one has two natural norms on Cc (Ω):
kf k∞
kf k2

:=

sup {|f (x)|},(II.9)

x∈Ω

:=

Z

Ω

n

2

d x |f (x)|

1/2

.

(II.10)The first norm is called the supremum-norm or sup-norm. The second norm is called theL2 -norm (see below). It is, of course, derived from the inner productZ(II.11)
dn x f (x)g(x).
(f, g) :=Ω

But even the first norm will turn out to play an important role in Hilbert space theory.
Interestingly, if Ω is compact, then Cc (Ω) = C(Ω) is complete in the norm k · k∞ . This claim
follows from the theory of uniform convergence, which is not part of this course (see [28]).10
However, Cc (Ω) fails to be complete in the norm k · k2 (see exercises): Consider Ω = [0, 1]. The
sequence of functions
(x ≤ 1/2)
 0n(x − 1/2) (1/2 ≤ x ≤ 1/2 + 1/n)
fn (x) :=
1(x ≥ 1/2 + 1/n)

is a Cauchy sequence with respect to k · k2 that converges to a discontinuous function f (x) = 0 forx ∈ [0, 1/2) and f (x) = 1 for x ∈ (1/2, 1] (the value at x = 1/2 is not settled; see below, but inany case it cannot be chosen in such a way that f is continuous).
8 More generally, Ω should be a so-called Borel subset of Rm .
9 The support of a function is defined as the smallest closed set outside which it vanishes.

10 To cover the noncompact case, we introduce the space C (Ω) that consists of all continuous functions on Ω that0vanish at infinity, in the sense that for each ǫ > 0 there is a compact subset K ⊂ Ω such that |f (x)| < ǫ for all x
outside K. Clearly, Cc (Ω) ⊆ C0 (Ω), with equality Cc (Ω) = C0 (Ω) = C(Ω) when Ω is compact. Now, when Ω isnoncompact it can be shown (by easy examples) that Cc (Ω) is not complete in the sup-norm; its completion turns
out to be C0 (Ω).

16

CHAPTER II. METRIC SPACES, NORMED SPACES, AND HILBERT SPACES

Clearly, Cc (Ω) lies in the space W of all functions f : Ω → C, and according to the above
scenario our task is to find a subspace H ⊂ W that plays the role of the completion of Cc (Ω) in
the norm k · k2 . There is a complication, however, which does not occur in the case of ℓ2 . Let us
ignore this complication first. A detailed study shows that the analogue of ℓ2 is now given by the
space L2 (Ω), defined as follows.
Definition II.7 The space L2 (Ω) consists of all functions f : Ω → C for which there exists a
Cauchy sequence (fn ) in Cc (Ω) with respect to k · k2 such that fn (x) → f (x) for all x ∈ Ω\N ,where N ⊂ Ω is a set of (Lebesgue) measure zero.
Recall (e.g. from [28], p. 110) that a subset N ⊂ Rn has measure zero if
Pfor any ǫ > 0 there
Pexistsa covering of N by an at most countable set (In ) of intervals for which n |In | <ǫ,
where
n |In |
Q
is the sum of the volumes of the In . (Here an interval in Rn is a set of the form nk=1 [ak , bk ]). For
example, any countable subset of Rn has measure zero, but there are others.
The space L2 (Ω) contains all functions f for which |f |2 is Riemann-integrable over Ω (so inparticular all of Cc (Ω), as was already clear from the definition), but many other, much wilder
functions. We can extend the inner product on Cc (Ω) to L2 (Ω) by means of(f, g) = lim (fn , gn ),n→∞(II.12)where (fn ) and (gn ) are Cauchy sequences as specified in the definition of L2 (Ω) (see footnote 7).
Consequently (see also footnote 6), taking gn = fn , the following limit exists:
kf k2 := lim kfn k2 .
n→∞(II.13)The problem is that (II.12) does not define an inner product on L2 (Ω) and that (II.13) does
not define a norm on it because these expressions fail to be positive definite. For example, take afunction f on Ω = [0, 1] that is nonzero in finitely (or even countably) many points. The Cauchy
sequence with only zeros defines f as an element of L2 (Ω), so kf k2 = 0 by (II.13), yet f 6= 0 as afunction. This is related to the following point: the sequence (fn ) does not define f except outside
a set of measure zero.
Everything is solved by introducing the space
L2 (Ω) := L2 (Ω)/N ,(II.14)

N := {f ∈ L2 (Ω) | kf k2 = 0.}(II.15)

where
Using measure theory, it can be shown that f ∈ N iff f (x) = 0 for all x ∈ Ω\N , where N ⊂ Ω issome set of measure zero. If f is continuous, this implies that f (x) = 0 for all x ∈ Ω.
It is clear that k · k2 descends to a norm on L2 (Ω) byk[f ]k2 := kf k2 ,(II.16)

where [f ] is the equivalence class of f ∈ L2 (Ω) in the quotient space. However, we normally workwith L2 (Ω) and regard elements of L2 (Ω) as functions instead of equivalence classes thereof. So in
what follows we should often write [f ] ∈ L2 (Ω) instead of f ∈ L2 (Ω), but who cares.
We would now like to show that L2 (Ω) is the completion of Cc (Ω). The details of the proof
require the theory of Lebesgue integration (see [26] or many other books, or the course of A. van
Rooij), but the idea is similar to the case of ℓ2 .
Let (fn ) be Cauchy in L2 (Ω). By definition of the norm in L2 (Ω), there is a sequence (hn ) inCc (Ω) such that:
1. kfn − hn k2 ≤ 2−n for all n;
2. |fn (x) − hn (x)| ≤ 2−n for all x ∈ Ω\An , where |An | ≤ 2−n .

II.3. ORTHOGONALITY AND ORTHONORMAL BASES

17

By the first property one can prove that (hn ) is Cauchy, and by the second that limn hn (x) exists
for almost all x ∈ Ω (i.e. except perhaps at a set N of measure zero). This limit defines a function
f : Ω\N → C for which hn (x) → f (x). Outside N , f can be defined in any way one likes. Hencef ∈ L2 (Ω), and its equivalence class [f ] in L2 (Ω) is independent of the value of f on the null set N .
It easily follows that limn fn = f in k · k2 , so that Cauchy sequence (fn ) converges to an elementof L2 (Ω). Hence L2 (Ω) is complete.
The identification of L2 (Ω) with the formal completion of Cc (Ω) is done in the same way as
before: we repeat (II.8), where this time the function f ∈ CL2 (Ω) is the one associated to the
Cauchy sequence (fn ) in Cc (Ω) through Definition II.7. As stated before, it would be really correct
to write (II.8) as follows:
U ([fn ]) := [f ] = [lim fn ],(II.17)nwhere the square brackets on the left-hand side denote equivalence classes with respect to theequivalence relation (fn ) ∼ (gn ) when limn kfn − gn k2 = 0 between Cauchy sequences in Cc (Ω),
whereas the square brackets on the right-hand side denote equivalence classes with respect to theequivalence relation f ∼ g when kf − gk2 = 0 between elements of L2 (Ω).
We finally note a interesting result about L2 (Ω) without proof (see [26], Satz 1.41, p. 43):
Theorem II.8 Every Cauchy sequence (fn ) in L2 (Ω) has a subsequence that converges pointwise
almost everywhere to some f ∈ L2 (Ω).
The proof of this theorem yields an alternative approach to the completeness of L2 (Ω).
In many cases, all you need to know is the following fact about L2 or L2 , which follows fromthe fact that L2 (Ω) is indeed the completion of Cc (Ω) (and is a consequence of Definition II.7) if
enough measure theory is used):
Proposition II.9 For any f ∈ L2 (Ω) there is a Cauchy sequence (fk ) in Cc (Ω) such that fk → f
in norm (i.e. limk→∞ kf − fk k2 = 0).
Without creating confusion, one can replace f ∈ L2 (Ω) by f ∈ L2 (Ω) in this statement, as long as
one keeps the formal difference between L2 and L2 in the back of one’s mind.

II.3

Orthogonality and orthonormal bases

As stressed in the Introduction, Hilbert spaces are the vector spaces whose geometry is closest tothat of R3 . In particular, the inner product yields a notion of orthogonality. We say that two
vectors f, g ∈ H are orthogonal, written f ⊥ g, when (f, g) = 0.11 Similary, two subspaces12
K ⊂ H and L ⊂ H are said to be orthogonal (K ⊥ L) when (f, g) = 0 for all f ∈ K and all g ∈ L.
A vector f is called orthogonal to a subspace K, written f ⊥ K, when (f, g) = 0 for all g ∈ K,
etc.
For example, if H = L2 (Ω) and Ω = Ω1 ∪ Ω2 , elementary (Riemann) integration theory showsthat the following subspaces are orthogonal:13
K

=

L

=

{f ∈ Cc (Ω) | f (x) = 0 ∀x ∈ Ω1 };{f ∈ Cc (Ω) | f (x) = 0 ∀x ∈ Ω2 }.

(II.18)(II.19)We define the orthogonal complement K ⊥ of a subspace K ⊂ H as
K ⊥ := {f ∈ H | f ⊥ K}.

(II.20)This set is automatically linear, so that the map K 7→ K ⊥ , called orthocomplementation, is anoperation from subspaces of H to subspaces of H. Clearly, H ⊥ = 0 and 0⊥ = H.
11 By definition of the norm, if f ⊥ g one has Pythagoras’ theorem kf + gk2 = kf k2 + kgk2 .
12 Recall that a subspace of a vector space is by definition a linear subspace.
13 This may be strengthened as follows: the space K consisting of all f ∈ L2 (Ω) that vanish for almost all x ∈ Ω11
is orthogonal to the space K2 consisting of all f ∈ L2 (Ω) that vanish for almost all x ∈ Ω2 . These subspaces are2closed in L (Ω), which is not the case for K and L in the main text.

18

CHAPTER II. METRIC SPACES, NORMED SPACES, AND HILBERT SPACES

Now, a subspace of a Hilbert space may or may not be closed. A closed subspace K ⊂ H ofa Hilbert space H is by definition complete in the given norm on H (i.e. any Cauchy-sequence in
K converges to an element of K).14 This implies that a closed subspace K of a Hilbert space H
is itself a Hilbert space if one restricts the inner product from H to K. If K is not closed already,we define its closure K as the smallest closed subspace of H containing K.15
For example, if Ω ⊂ Rn then Cc (Ω) is a subspace of L2 (Ω) which is not closed; its closure is2
L (Ω).16
Closure is an analytic concept, related to convergence of sequences. Orthogonality is a geometricconcept. However, both are derived from the inner product. Hence one may expect certain
connections relating analysis and geometry on Hilbert space.
Proposition II.10 Let K ⊂ H be a subspace of a Hilbert space.
1. The subspace K ⊥ is closed, withK⊥ = K⊥= K ⊥.

(II.21)

2. One has
K ⊥⊥ := (K ⊥ )⊥ = K.

(II.22)

3. Hence for closed subspaces K one has K ⊥⊥ = K.
The proof is an exercise.
We now turn to the concept of an orthonormal basis (o.n.b.) in a Hilbert space. First, onecan:
1. Define a Hilbert space H to be finite-dimensional if P
has a finite o.n.b. (ek ) in the sensethat (ek , el ) = δkl and any v ∈ H can be written as v = k vk ek for some vk ∈ C;
2. Prove (by elementary linear algebra) that any o.n.b. in a finite-dimensional Hilbert space H
has the same cardinality;
3. Define the dimension of H as the cardinality of an arbitary o.n.b. of H.
P
It is trivial to show that if v = k vk ek , thenvk = (ek , v)

(II.23)|(ek , v)|2 = kvk2 .

(II.24)andX
kThis is called Parseval’s equality; it is a generalization of Pythagoras’s Theorem. Note that if
H is finite-dimensional, then any subspace is (automatically) closed (exercise).
Now what happens when H is not finite-dimensional? In that case, it is called infinitedimensional. The spaces ℓ2 and L2 (Ω) are examples of infinite-dimensional Hilbert spaces. We
call an infinite-dimensional Hilbert space separable when it contains a countable orthonormal set(ek )k∈N such that any v ∈ H can be written as
v=

∞
X

vk ek

(II.25)k=1
14 Since H is a Hilbert space we know that the sequence has a limit in H, but this limit may not lie in K evenwhen all elements of the sequence lie in K. This possibility arises precisely when K fails to be closed.
15 This closure is isomorphic to the abstract completion of K as explained before.
16 More precisely, as we have seen C (Ω) is really a subspace of L2 (Ω), so what is meant here is that the collection
cof equivalence classes of functions in Cc (Ω) is a non-closed subspace of L2 (Ω). But notations such as Cc (Ω) ⊂ L2 (Ω),
though strictly speaking false, are not misleading and will often be used in what follows. In fact, if f ∈ Cc (Ω) thenthe equivalence class [f ] ∈ L2 (Ω) contains a unique element that is continuous, namely f itself!

II.3. ORTHOGONALITY AND ORTHONORMAL BASES

19

for some vk ∈ C. By definition, this means thatv = limN →∞where the limit means that
lim kv −

N →∞

N
X

vk ek

(II.26)

vk ek k = 0.

(II.27)

k=1

N
X

k=1

Here the norm is derived from the inner product in the usual way. Such a set is again called anorthonormal basis. It is often convenient to take Z instead of N as the index set of the basis, sothat one has (ek )k∈Z andNXv = limvk ek .
(II.28)N →∞k=−N

It is an exercise to show that (II.23) and (II.24) are still valid in the infinite-dimensional case.
Also, the following lemma will often be used:
Lemma II.11 Let (ek ) be an o.n.b. in an infinite-dimensional separable Hilbert space H and let
f, g ∈ H. ThenX
(f, ek )(ek , g) = (f, g).
(II.29)kThis follows if one expands f and g on the right-hand side according to (II.25) and uses (II.23);
one has to be a little bit careful with the infinite sums but these complications are handled in thesame way as in the proof of (II.23) and (II.24).
The following result is spectacular:17
Theorem II.12
dimension.

1. Two finite-dimensional Hilbert spaces are isomorphic iff they have the same

2. Any two separable infinite-dimensional Hilbert spaces are isomorphic.
The proof, though, is left as an exercise. It relies on the choice of a basis in each of the two
spaces under consideration. To illustrate the theorem, we show that ℓ2 (Z) and L2 ([−π, π]) are
isomorphic through the Fourier transform. Namely, using Fourier theory (see [28]) one can showthat the functions (ek )k∈Z defined by1ek (x) := √ eikx
2π(II.30)from an o.n.b. of L2 ([−π, π]). Trivially, the functions (ϕk )k∈Z defined byϕk (l) := δkl(II.31)

form an o.n.b of ℓ2 (Z). (If one regards an element of ℓ2 (Z) as a sequence instead of a function,
fk is the sequence with a 1 at position k and zero’s everywhere else.) This shows that ℓ2 (Z)and L2 ([−π, π]) are both separable infinite-dimensional, and hence isomorphic by Theorem II.12.
Indeed, it is trivial to write down the unitary map U : L2 ([−π, π]) → ℓ2 (Z) that ‘makes’ ℓ2 (Z) andL2 ([−π, π]) isomorphic according to the definition of isomorphism: one simply puts
Z π
1
U f (k) := (ek , f )L2 = √dx e−ikx f (x).
(II.32)2π −π17 The general statement is as follows. One can introduce the notion of an orthonormal basis for an arbitrary
Hilbert space as a maximal orthonormal set (i.e. a set of orthonormal vectors that is not properly contained in any
other orthonormal set). It is an exercise to show that in the separable case, this notion of a basis is equivalent to
the one introduced in the main text. One then proves that any two orthonormal bases of a given Hilbert spacehave the same cardinality. Hence one may define the dimension of a Hilbert space as the cardinality of an arbitrary
orthonormal basis. Theorem II.12 then reads in full glory: Two Hilbert spaces are isomorphic iff they have the same
dimension. See [18].

20

CHAPTER II. METRIC SPACES, NORMED SPACES, AND HILBERT SPACES

Here f ∈ L([−π, π]). The second equality comes from the definition of the inner product in
L2 ([−π, π]). The inverse of U is V : ℓ2 (Z) → L2 ([−π, π]), given by
X
V ϕ :=
ϕ(k)ek ,(II.33)k∈Z

where ϕ ∈ ℓ2 (Z). It is instructive to verify that V = U −1 :X
X
X(U V ϕ)(k) = (ek , V ϕ)L2 = (ek ,ϕ(l)el )L2 =ϕ(l)(ek , el )L2 =ϕ(l)δkl = ϕ(k),lllwhere one justifies taking the infinite
sum over l out of the inner product by the Cauchy-SchwarzPinequality (using the fact that l kϕ(l)k2 < ∞, since by assumption ϕ ∈ ℓ2 (Z)). Similarly, forf ∈ L2 ([−π, π]) one computes
X
X
V Uf =(U f )(k)ek =(ek , f )L2 ek = fkkby (II.25) and (II.23). Of course, all the work is in showing that the functions ek form an o.n.b. of
L2 ([−π, π]), which we have not done here!
Hence V = U −1 , so that (II.33) reads
U −1 ϕ(x) =1 X
ϕ(k)eikx .
ϕ(k)ek (x) = √
2π
k∈Z
k∈Z
X

(II.34)

Finally, the unitarity of U follows from the computation (where f, g ∈ L2 )X
(U f, U g)ℓ2 =(f, ek )L2 (ek , g)L2 = (f, g)L2 ,kwhere we have used (II.29).
The choice of a basis in the argument that ℓ2 (Z) ∼= L2 ([−π, π]) was clearly essential. There arepairs of concrete Hilbert spaces, however, which one can show to be isomorphic without choosing
bases. A good example is provided by (II.8) and surrounding text, which proves the “practical”
completion ℓ2 of ℓc and the formal completion ℓ˜c to be isomorphic. If one can find a unitary map
U : H1 → H2 without choosing bases, the two Hilbert spaces in question are called naturally^
isomorphic. As another example, the formal completion Cc (Ω) of Cc (Ω) is naturally isomorphic
to L2 (Ω).

Chapter III

Operators and functionals
III.1

Bounded operators

For the moment, we are finished with the description of Hilbert spaces on their own. Indeed,
Theorem II.12 shows that, taken by themselves, Hilbert spaces are quite boring. The subject
comes alive when one studies operators on Hilbert spaces. Here an operator a : H1 → H2
between two Hilbert space is nothing but a linear map (i.e., a(λv + µw) = λa(v) + µa(w) for all
λ, µ ∈ C and v, w ∈ H1 ). We usually write av for a(v).
The following two special cases will occur time and again:
1. Let H1 = H and H2 = C: a linear map ϕ : H → C is called a functional on H.
2. Let H1 = H1 = H: a linear map a : H → H is just called an operator on H.
To construct an example of a functional on H, take f ∈ H and define ϕf : H → C by
ϕf (g) := (f, g).

(III.1)

When H is finite-dimensional, any operator on H can be represented by a matrix and thetheory reduces to linear algebra. For an infinite-dimensional example, take H = ℓ2 and â ∈ ℓ∞ . It
is an exercise to show that if f ∈ ℓ2 , then âf ∈ ℓ2 . Hence may define an operator a : ℓ2 → ℓ2 byâf := af.

(III.2)

We will often write a for this operator instead of â. Similarly, take H = L2 (Ω) and â ∈ Cb (Ω) (whereCb (Ω) is the space of bounded continuous functions on Ω ⊂ Rn , i.e. â : Ω → C is continuous andkâk∞ < ∞). As in the previous example, it is an exercise to show that if f ∈ L2 (Ω) and â ∈ Cb (Ω),
then âf ∈ L2 (Ω).1 Thus also in this case (III.2) defines an operator a : L2 (Ω) → L2 (Ω), called amultiplication operator.
Finally, the operators U and V constucted at the end of the previous chapter in the context of
the Fourier transform give examples of operators between different Hilbert spaces.
As in elementary analysis, where one deals with functions f : R → R, it turns out to be useful
to single out functions with good properties, notably continuity. So what does one mean by a‘continuous’ operator a : H1 → H2 ? One answer come from topology: the inner product on a
Hilbert space defines a norm, the norm defines a metric, and finally the metric defines a topology,
so one may use the usual definition of a continuous function f : X → Y between two topologicalspaces.2 Since we do not require students of this course to be familiar with abstract topology,
we use another definition, which turns out to be equivalent to the topological one. (In fact, thedefinition below is much more useful than the topological definition.)
1 The easiest way to do this exercise is to start with f ∈ C (Ω); the fact that âf ∈ L2 (Ω) then follows from ancelementary estimate from Riemann integration theory. One then passes to the general case by an approximationargument using Proposition II.9.
2 Recall that f is called continuous when f −1 (O) is open in X for any open set O in Y .

21

22

CHAPTER III. OPERATORS AND FUNCTIONALSDefinition III.1 a : H1 → H2 be an operator. Define a positive number kak by(III.3)
kak := sup {kavkH2 , v ∈ H1 , kvkH1 = 1},pwhere kvkH1 = (v, v)H1 , etc. We say that a is continuous or bounded when kak < ∞.

For the benefit of those familiar with topology, we mention without proof that a is continuous
according to this definition iff it is continuous in the topological sense, as explained above. Thismay be restated as follows: an operator a : H1 → H2 is continuous (in the topological sense) iff it
is bounded (in the sense of Definition III.1).
Geometrically, the set {v ∈ H1 , kvkH1 = 1} is the unit ball in H1 , i.e. the set of vectors oflength 1 in H1 . Hence kak is the supremum of the function v 7→ kavkH2 from the unit ball in H1 to
R+ . If H1 is finite-dimensional the unit ball in H1 is compact. Since the function just mentioned
is continuous, it follows that any operator a on a finite-dimensional Hilbert space is bounded (asa continuous function from a compact set in Rn to R assumes a maximum somewhere).3
If a is bounded, the number kak is called the norm of a. This terminology remains to bejustified; for the moment it is just a name. It is easy to see that if kak < ∞, the norm of acoincided with the constant
kak = inf {C ≥ 0 | kavkH2 ≤ CkvkH1 ∀v ∈ H1 }.

(III.4)Moreover, if a is bounded, then it is immediate that
kavkH2 ≤ kak kvkH1(III.5)for all v ∈ H1 . This inequality is very important. For example, it trivially implies thatkabk ≤ kakkbk,(III.6)

where a : H → H and b : H → H are any two bounded operators, and ab := a ◦ b, so that(ab)(v) := a(bv).
In the examples just considered, all operators turn out to be bounded. First take a functional
ϕ : H → C; since k · kC = | · |, one has
kϕk := sup {|ϕ(v)|, v ∈ H, kvkH = 1}.

(III.7)

If one uses Cauchy–Schwarz, it is clear from (III.1) that kϕf k ≤ kf kH . In fact, an important resultin Hilbert space theory says:
Theorem III.2 Let H be a Hilbert space. Any functional of the form ϕf for some f ∈ H (see(III.1)) is continuous. Conversely, any continuous functional ϕ : H → C is of the form ϕf : g 7→
(f, g) for some unique f ∈ H, and one has
kϕf k = kf kH .

(III.8)The proof is as follows. First, given f ∈ H, as already mentioned, ϕf is bounded by Cauchy–
Schwarz. Conversely, take a continuous functional ϕ : H → C, and let N be the kernel of ϕ. Thisis a closed subspace of H by the boundedness of ϕ. If N = H then ϕ = 0 so we are ready, sinceϕ = ϕf =0 . Assume N 6= H. Since N is closed, N ⊥ is not empty, and contains a vector h withkhk = 1.4 For any g ∈ H, one has ϕ(g)h − ϕ(h)g ∈ N , so (h, ϕ(g)h − ϕ(h)g) = 0, which means
ϕ(g)(h, h) = ϕ(h)(h, g), or ϕ(g) = (f, g) with f = ϕ(h)h.
To prove uniqueness of f , suppose there is h′ with h′ ∈ ker(ϕ)⊥ and kh′ k = 1, and consequently
also ϕ(g) = (f ′ , g) with f ′ = ϕ(h′ )h′ . Then ϕ(h) = ϕ(h′ )(h′ , h), so that h − (h′ , h)h′ ∈ ker(ϕ).
3 If a : Cn → Cn is linear, can be shown from the minimax-property of eigenvalues that kak2 coincides with thelargest eigenvalue of a∗ a.
P
4 To see this, pick an orthonormal basis (e ) of N . Since N is closed, any f of the form f =nn cn en , cn ∈ C,P
2 < ∞) actually lies in N . Since N 6= H, there exists g ∈that lies in H (which
is
the
case
iff
|c
|
/ N , which
n nP
P
implies that g 6= n (en , g)en , or h := g − n (en , g)en 6= 0. Clearly, h ∈ N ⊥ , and since h 6= 0 the vector h/khk
has norm 1. This argument will become clearer after the introduction of projections later in this chapter.

23

III.1. BOUNDED OPERATORS

But ker(ϕ)⊥ is a linear subspace of H, so it must be that h − (h′ , h)h′ ∈ ker(ϕ)⊥ as well. Sinceker(ϕ)⊥ ∩ ker(ϕ) = 0, it follows that h − (h′ , h)h′ = 0. Hence h = (h′ , h)h′ and therefore kh′ k = 1
and khk = 1 yield |(h, h′ )| = 1, or (h′ , h)(h′ , h) = 1. It follows thatf = ϕ(h)h = (h′ , h)ϕ(h′ )(h′ , h)h′ = ϕ(h′ )h′ = f ′ .

To compute kϕf k, first use Cauchy–Schwarz to prove kϕf k ≤ kf k, and then apply ϕf to f toprove equality.

For an example of a bounded operator a : H → H, note that on ℓ2 as well as on L2 (Ω) the
operator â defined by (III.2) is bounded, withkak = kâk∞ .

(III.9)The proof of this result is an exercise. This exercise involves the useful estimate
kaf k2 ≤ kâk∞ kf k2,(III.10)which in turn follows from (III.9) (supposing one already knew this) and (III.5).
Finally, the operators U and V at the end of the previous chapter are unitary; it easily follows
from the definition of unitarity thatkU k = 1(III.11)

for any unitary operator U .
What about discontinuous or unbounded operators? In view of (III.9), let us take an unbounded
function â : Z → C and attempt to define an operator a : ℓ2 → ℓ2 by means of (III.2), hoping thatkak = ∞. The problem with this attempt is that in fact an unbounded function does not define
a map from ℓ2 to ℓ2 at all, since âf will not be in ℓ2 for many choices of f ∈ ℓ2 . (For example,
consider â(k) = k and find such an f for which âf ∈/ ℓ2 yourself.) This problem is generic: as soon
as one has a candidate for an unbounded operator a : H1 → H2 , one discovers that in fact a does
not map H1 into H2 .
Nonetheless, unbounded operators occur naturally in many examples and hence are extremely
important in practice, especially in quantum mechanics and the theory of (partial) differential
equations. But they are not constructed in the above manner as maps from H1 to H2 . To prepare
for the right concept of an unbounded operator, let us look at the bounded case once more. We
restrict ourselves to the case H1 = H2 = H, as this is the relevant case for quantum mechanics.
As before, we denote the completion or closure of a subspace D of a Hilbert space H by D ⊂ V .
Proposition III.3 Let D ⊂ H be a subspace of a Hilbert space, and let a : D → H be a linear
map. Define the positive number
kakD := sup {kavkH , v ∈ D, kvkH = 1}.

(III.12)

If kakD < ∞, there exists a unique bounded extension of a to an operator a− : D → H withka− k = kakD .

(III.13)

In particular, when D is dense in H (in the sense that D = H), the extension a− is a bounded
operator from H to H.
Conversely, a bounded operator a : H → H is determined by its restriction to a dense subspace
D ⊂ H.

The proof is an exercise. The idea is to define a− v for v ∈/ D by a− v := limn avn , where(vn ) ⊂ D converges to v. We leave the easy details to the reader. Hence in the above examples it
suffices to compute the norm of a in order to find the norm of a− .
The point is now that unbounded operators are defined as linear maps a : D → H for which
kakD = ∞. For example, take â ∈/ ℓ∞ and f ∈ D = ℓc . Then af = âf ∈ ℓc , so that a : ℓc → ℓ2
/ ℓ∞ (i.e. â is unbounded). Anotheris defined. It is an exercise to show that kakℓc = ∞ iff â ∈(1)2
example is af := df /dx, defined on f ∈ C ([0, 1]) ⊂ L ([0, 1]). Once again, it is an exercise toshow that kd/dxkC (1) ([0,1]) = ∞. In quantum mechanics, operators like position, momentum andthe Hamiltonian of a particle in a potential are unbounded (see exercises).

24

CHAPTER III. OPERATORS AND FUNCTIONALS

III.2

The adjoint

Now let H be a Hilbert space, and let a : H → H be a bounded operator. The inner product on
H gives rise to a map a 7→ a∗ , which is familiar from linear algebra: if H = Cn , so that, uponchoosing the standard basis (ei ), a is a matrix a = (aij ) with aij = (ei , aej ), then the adjoint isgiven by a∗ = (aji ). In other words, one has(a∗ f, g) = (f, ag)(III.14)

for all f, g ∈ Cn . This equation defines the adjoint also in the general case, but to prove existenceof a∗ Theorem III.8 is needed: for fixed a ∈ B(H) and f ∈ H, one defines a functional ϕaf : H → C
by ϕaf (g) := (f, ag). This functional is bounded by Cauchy–Schwarz and (III.5):|ϕaf (g)| = |(f, ag)| ≤ kf kkagk ≤ kf kkakkgk,
so kϕaf k ≤ kf kkak. Hence by Theorem III.8 there exists a unique h ∈ H such that ϕaf (g) = (h, g)
for all g ∈ H. Now, for given a the association f 7→ h is clearly linear, so that we may definea∗ : H → H by a∗ f := h; eq. (III.14) then trivially follows. Note that the map a 7→ a∗ is anti-linear:
one has (λa)∗ = λa for λ ∈ C.
It is an exercise to show that for each a ∈ B(H) one haska∗ kka∗ ak= kak;= kak2 .

(III.15)(III.16)

A bounded operator a : H → H is called self-adjoint5 when a∗ = a. It immediately follows
form (III.14) that for self-adjoint a one has (f, af ) ∈ R.6
One may also define self-adjointness in the unbounded case, in a way very similar to the story
above. Namely, let D ⊂ H be dense and let a : D → H be a possibly unbounded operator. We
write D(a) for D and define an operator a∗ : D(a∗ ) → H as follows.
Definition III.4
1. The adjoint a∗ of an unbounded operator a : D(a) → H has domain D(a∗ )consisting of all f ∈ H for which the functional g 7→ ϕaf (g) := (f, ag) is bounded. On thisdomain, a∗ is defined by requiring (a∗ f, g) = (f, ag) for all g ∈ D(a).
2. The operator a is called self-adjoint when D(a∗ ) = D(a) and a∗ = a.
Here the vector a∗ f once again exists by the Theorem III.8 (indeed, the definition has beenformulated precisely so as to guarantee that a∗ f exists!), and it is uniquely determined by ourassumption that D(a) be dense in H. However, this time we cannot conclude that D(a∗ ) =H, as in the bounded case. Indeed, it may even happen that D(a∗ ) is zero! However, this ispretty pathological, and in most ‘natural’ examples D(a∗ ) turns out to be dense. For example, amultiplication operator â ∈ C(Ω) on H = L2 (Ω), defined on the domain D(a) = Cc (Ω) by af = âf
as usual, has a∗ = a (i.e., the complex conjugate of a seen as a multiplication operator) defined on
the domain D(a∗ ) given byD(a∗ ) = {f ∈ L2 (Ω) | af ∈ L2 (Ω)}.

(III.17)Since D(a) ⊂ D(a∗ ), the operator a cannot be self-adjoint. However, if we start again anddefine a on the domain specified by the right-hand side of (III.17), it turns out that this time one
does have a∗ = a. We will study such questions in detail later on, as they are very important forquantum mechanics.
We return to the bounded case.
5 Or Hermitian.
6 In quantum mechanics self-adjoint operators model physical observables, so that these have real expectation
values.

25

III.3. PROJECTIONS

III.3

Projections

The most important examples of self-adjoint operators are projections.
Definition III.5 A projection on a Hilbert space H is a bounded operator p ∈ B(H) satisfying
p2 = p∗ = p.
To understand the significance of projections, the reader should first recall the discussion about
orthogonality and bases in Hilbert spaces in Chapter II.II.3. Now let K ⊂ H be a closed subspaceof H; such a subspace is a Hilbert space by itself, and therefore has an orthonormal basis (ei ).
Applying (II.25) with (II.23) to K, it is easy to verify thatX(ei , f )ei(III.18)
p : f 7→ifor each f ∈ H, where the sum converges in H,7 defines a projection. Clearly,
pf
pf

=
=

f for f ∈ K;
0 for f ∈ K ⊥ .

(III.19)

Proposition III.6 For each closed subspace K ⊂ H one has H = K ⊕ K ⊥ . In other words, given
any closed subspace K ⊂ H each f ∈ H has a unique decomposition f = f k + f ⊥ , where f k ∈ K
and f ⊥ ∈ K ⊥ .
The existence of the decomposition is given by f k = pf and f ⊥ = (1 − p)f , and its uniquenessfollows by assuming f = g k + g ⊥ with g k ∈ K and g ⊥ ∈ K ⊥ : one then has f k − g k = f ⊥ − g ⊥ , butsince the left-hand side is in K and the right-hand side is in K ⊥ , both sides lie in K ∩K ⊥ = 0.
Conversely, given a projection p, define K := pH. This is a closed subspace of H: if f ∈ pHthen f = pg for some g ∈ H, but then pf = p2 g = pg = f , so that f ∈ pH iff pf = f . If fn → f
for fn ∈ pH, then pf = limn pfn = lim fn = f , hence f ∈ pH. Furthermore, K ⊥ = (1 − p)H;
verifying this fact uses both p∗ = p and p2 = p. Defining f k := pf and f ⊥ := (1 − p)f , one clearlyhas f = f k + f ⊥ with f k ∈ K and f ⊥ ∈ K ⊥ , so this is the unique decomposition of f described in
Proposition III.6. Hence f k is given, for arbitrary f ∈ H, by both the right-hand side of (III.18)
and by pf , so that (III.18) holds and p is completely characterized by its image. Hence we have
proved:Theorem III.7 There is a bijective correspondence p ↔ K between projections p on H and closed
subspaces K of H: given a projection p one puts K := pH, and given a closed subspace K ⊂ H
one defines p by (III.18), where (ei ) is an arbitrary orthonormal basis of K.
An important special case of a projection is the unit operator p = 1, associated with K = H.
Projections are important in many ways. One is their occurrence in the spectral theorem,
which will occupy us for the remainder of the course. For the moment, let us mention that the
spectral theorem of linear algebra has an elegant reformulation in terms of projections. In the usual
formulation, a matrix a : Cn → Cn satisfying a∗ a = aa∗ has an o.n.b. of eigenvectors {ei }i=1,...,n ,i.e. one has aei = λi ei for some λi ∈ C. In the list of eigenvalues {λi }i=1,...,n , some may coincide.
Now make a list {λα }α=1,...,m≤n where all the λα ’s are different. Let pα be the projection onto
the subspace Hα = pα Cn of all f ∈ Cn for which af = λα f ; of course,PHα is the linear span ofthose ei for which λα = λi . The spectral theorem now states that a = α λα pα . In other words,
each normal matrix is a linear combination of mutually orthogonal projections. We will see indue course what remains of this theorem if one passes from Cn to an arbitrary (separable) Hilbert
space.
Furthermore, it follows from Proposition III.6 that f ∈ H is an eigenvector of p iff f ∈ pH orf ∈ (pH)⊥ ; in the first case the eigenvector is 1, and in the second case it is 0.
7 The sum does not converge in the operator norm unless it is finite.

26

CHAPTER III. OPERATORS AND FUNCTIONALS

Apart from projections, another important class of operators on a Hilbert space consists of theunitaries. An operator u : H → H is called unitary when uu∗ = u∗ u = 1. Equivalently, u is−1
isometric, in that (uf, uf ) = (f, f ) for all f ∈ H, and invertible (with inverseu∗ ). ForP u =P
example, if (ei ) and (ui ) are two orthonormal bases of H, then the operator u( i ci ei ) := i ci ui isunitary. In quantum mechanics, one usually encounters unitary operators of the form u = exp(ia),where a is self-adjoint and (for bounded a) the exponential is defined by its usual power series
expansion, which converges in operator norm. Clearly, one has u∗ = exp(−ia) and since forcommuting a and b (that is, ab = ba) one has exp(a + b) = exp(a) exp(b), one sees immediatelythat u is indeed unitary.8
A partial isometry is an operator v for which v ∗ v = p is a projection. A special case is an
isometry, characterized by p = 1, i.e., v ∗ v = 1. An invertible isometry is clearly unitary. The
structure of partial isometries is as follows.
Proposition III.8 If v is a partial isometry, then v ∗ is a partial isometry as well. Let the associated projection be q := vv ∗ . The kernel of v is (pH)⊥ , and its range is qH. The operator v is
unitary from pH to its range qH and zero on (pH)⊥ . Conversely, any partial isometry has thisform for projections p and q.
The proof is an exercise.

8 In quantum mechanics the operator a is generally unbounded, a case we will deal with in great detail later on.

Chapter IV

Compact operators
IV.1

Linear algebra revisited

Compact operators on a Hilbert space (or, more generally, on a Banach space) are special bounded
operators that behave like matrices on Cn in many ways. To make this point, we first recall
the proof that any hermitian matrix (aij ) (i.e., satisfying aji = aij ) can be diagonalized. In
linear algebra this theorem is usually stated in a basis-dependent form. From our more abstract
perspective of operators, the matrix (aij ) arises from the operator a : Cn → Cn through the choiceof an arbitrary orthonormal basis (ei ), in terms of which one has aij = (ei , aej ). The spectral
theorem then states that Cn has a (possibly) new basis of eigenvectors (ui ), in which a is diagonal:
with respect to this basis one has ãij = (ui , auj ) = (ui , λj uj ) = λi δij , where λi are the eigenvaluesof a (possibly degenerate). Now, this result can be restated without any reference to the notion ofa basis, as follows.
Proposition IV.1 Let H = Cn be a finite-dimensional Hilbert space, and let Let a : H → H bea self-adjoint operator on H. There exists a family
projections (pα ) (i.e.,P
P of mutually orthogonalpα H ⊥ pβ H for α 6= β, or pα pβ = δαβ ) with α pα = 1 and a = α λα pα , where λα are theeigenvalues of a. In other words, pα is the projection onto the eigenspace in H with eigenvalue λα ;the dimension of pα H is equal to the multiplicity of the eigenvalue λα .
The key to the proof is the following lemma.
Lemma IV.2 Every self-adjoint operator a on H = Cn has an eigenvector with associated eigenvalue λ satisfying |λ| = kak.
Note that by definition of the operator norm an eigenvalue cannot possibly be any bigger!
The proof uses some topology, but in the restricted context of H = Cn we simply say (Heine–
Borel) that a set is compact when it is closed and bounded. We will use the following facts:
Lemma IV.3
compact.1

1. The image of a compact set in H under a continuous map into H or C is

2. A continuous function f : K → R on a compact set K attains a maximum and a minimum.
We now prove Lemma IV.2. First, the unit ball B1 ⊂ H, defined in general by
B1 := {f ∈ H | kf k ≤ 1},(IV.1)

is clearly compact in H = Cn . We know from basic analysis that any linear map a : Cn → Cn
is continuous,2 so that aB1 is compact as well. The norm f 7→ kf k is continuous on H, hence
1 This true in general: if f : K → Y is a continuous map between a compact space K and an arbitrary topologicalspace Y , then f (K) is compact in Y .
2 A linear map V → V on a normed vector space is continuous iff it is continuous at zero. The latter easily follows
for V = Cn . The map is therefore bounded by definition (cf. (III.3)).

27

28

CHAPTER IV. COMPACT OPERATORS

it follows that the function f 7→ kaf k2 attains a maximum on B1 , say at f = f0 , obviouslywith kf0 k = 1. By definition of the norm (cf. (III.3)), this maximum must be kak2 , so thatkak2 = kaf0 k2 . Cauchy–Schwarz and a∗ = a then yieldkak2 = kaf0 k2 = (af0 , af0 ) = (f0 , a2 f0 ) ≤ kf0 kka2 f0 k ≤ ka2 k = kak2 ,where we have used (III.16). In the Cauchy–Schwarz inequality (I.5) one has equality iff g = zf
for some z ∈ C, so that we must have a2 f0 = zf0 , with |z| = kak2 . Moreover, z ∈ R, as for any
self-adjoint operator eigenvalues must be real (trivial exercise!), so a2 f0 = λ2 f0 with either λ = kakor λ = −kak. Now either af0 = λf0 , in which case the lemma is proved, or g0 := af0 − λf0 6= 0.
In the latter case,ag0 = a2 f0 − λaf0 = λ2 f0 − λaf0 = −λg0 ,and the lemma follows, too.

Given this lemma, the proof of Proposition IV.1 is peanuts. If (g, f0 ) = 0 then(ag, f0 ) = (g, a∗ f0 ) = (g, af0 ) = ±λ(g, f0 ) = 0,
so that a maps f0⊥ into itself. In other words, if p is the projection onto f0⊥ then pa = ap and
pa = pap is the restriction of a to f0⊥ = pH. We now iterate the above procedure: we apply exactly
the same reasoning to pa as an operator on pH, finding an eigenvector f1 of pa, which of course
is an eigenvector of a, as a = pa on pH. We then form the orthogonal complement of f1 in pH,
etcetera. Since H is finite-dimensional, this procedure ends after n steps, leaving us with a basis{f0 , . . . , fn−1 } of H that entirely consists of eigenvectors by construction. Finally, we assemble
those eigenvectors fk with the same eigenvalue λα and define pα to be the projection onto theirlinear span Kα (i.e., pα is given by (III.18) applied to K = Kα ).


IV.2

The spectral theorem for self-adjoint compact operators

Let H be infinite-dimensional (and separable).3 Eigenvectors and eigenvalues of operators a on a
Hilbert space H are defined in the same way as for H = Cn : if af = λf for some λ ∈ C then f ∈ H
is called an eigenvector of a with eigenvalue λ. A crucial difference with the finite- dimensionalsituation is that even a bounded self-adjoint operator on an infinite-dimensional Hilbert space may
not have any eigenvectors (let alone a basis of them!). For example, on H = L2 (Ω) a multiplication
operator defined by a nonzero continuous function has no eigenfunctions at all. The idea is now
to define a class of operators on H for which the proof of Proposition IV.1 can be copied, so thatthe existence of a complete set of eigenvectors is guaranteed.
We will once again need some topology, but in our setting of separable Hilbert spaces we may
keeps things simple: a set K in a separable Hilbert space H is compact when every sequence inK has a convergent subsequence, and a map α : H → T , where T = C or T = H, is continuous
if it “preserves limits,” i.e., if fn → f in H then α(fn ) → α(f ) in T .4 For H = Cn , thisnotion of compactness is equivalent to the Heine–Borel property; for separable infinite-dimensional
H this equivalence no longer holds. Our notion of continuity is equivalent to the usual one in
topology. The norm f 7→ kf k is continuous on H, which is tautological given our definition ofcontinuity, because convergence in H has been defined in terms of the norm! I.e., fn → f in H
means kfn − f k → 0, which precisely expresses continuity of the norm. Similarly, according to ourdefinition a bounded operator a : H → H is clearly continuous, too, for kafn − af k → 0 because
kafn − af k ≤ kakkfn − f k by (III.5). The main point is that Lemma IV.3 still applies.
Definition IV.4 A compact operator on a Hilbert space H is a bounded operator that maps
the unit ball B1 ⊂ H into a compact set.
3 The main theorem below is true in general as well, with a similar proof involving more topology.
4 Compactness and continuity may be defined for arbitrary topological spaces in this way if one replaces sequencesby nets.

IV.2. THE SPECTRAL THEOREM FOR SELF-ADJOINT COMPACT OPERATORS

29

This is not the case for any bounded operator, for althoug such operators are continuous, theunit ball in H is not compact.5 If H is finite-dimensional, so that B1 is compact, then any operator
is compact. More generally, a finite-rank operator on an infinite-dimensional Hilbert space (i.e.,
an operator whose image is finite-dimensional) is compact.
Proposition IV.5 A bounded operator on a separable Hilbert space is compact iff it is the normlimit of a sequence of finite-rank operators.
The proof is left as an exercise, as is the following consequence of the proposition.
Corollary IV.6

1. If a is compact then so is its adjoint a∗ ;2. If a is compact and b is bounded then ab and ba are compact;
3. A projection p is compact iff it is finite-dimensional.
Using this corollary, it is easy to show that the set of all compact operators in B(H) is a
Banach space called B0 (H) in the operator norm. Moreover, B0 (H) is even an algebra underoperator multiplication, closed under involution.
Integral operators form an important class of compact operators.
Without proof we mention
R
that if Ω ⊂ Rn is compact, any operator of the form af (x) = Ω dn x a(x, y)f (y) is compact when
the kernel a(−, −) is continuous on Ω × Ω. More
for any Ω ⊂ Rn such an operator is
R generally,2nncompact when a(−, −) ∈ L (Ω × Ω), i.e., when d xd y |a(x, y)|2 < ∞. The following theorem
completely characterizes self-adjoint compact operators.
Theorem IV.7 Let a be a self-adjoint compact operator on a Hilbert space H. Then H has an
orthonormal basis (ei ) of eigenvectors of a, in terms of whichX
λi pi ,
(IV.2)a=iwhere pi projects onto the span of ei , and the sum converges in the sense that af =each fixed f ∈ H. Moreover, the set (λi ) of eigenvalues of a has the property thatPi λi pi f forlim |λi | = 0.

(IV.3)i→∞

Conversely, a bounded self-adjoint operator on H of the form (IV.2) where the eigenvalues
satisfy (IV.3) is compact.
To prove the first claim, we may simply repeat the proof of Proposition IV.1, as compact operators
have been defined for precisely this purpose! In particular, Lemma IV.2 still holds. However, theinfinite-dimensional situation makes one big difference: P
the iteration procedure in the proof is nota priori guaranteed to converge. To settle this, let p = i pi (with convergence as understood in(III.18)) be the projection onto the subspace of H spanned by all eigenvectors of a. Clearly, a maps
pH onto itself, and using a∗ = a, it follows that it maps (pH)⊥ onto itself as well: if g ∈ (pH)⊥then by definition (f, g) = 0 for all f ∈ pH, so (f, ag) = (a∗ f, g) = (af, g) = 0. But (pH)⊥ is a
Hilbert space in its own right, and since the restriction of a to (pH)⊥ is given by (1 − p)a, thisrestriction is still compact by Corollary IV.6.2. Hence Lemma IV.2 applies to it, implying that
(1 − p)a has an eigenvector in (pH)⊥ , which is the same as saying that a has an eigenvector in(pH)⊥ . This contradicts the definition of pH.
We leave the proof of the second claim as an exercise.
Corollary IV.8 Each eigenvalue of a self-adjoint compact operator except possibly 0 has finite
multiplicity, and 0 is the only possibly accumulation point of the set of eigenvalues. Hence we may
rewrite (IV.2) asXa=λα pα ,
(IV.4)
αwhere all eigenvalues λα are different and pα is the finite-dimensional projection pα :=Pi|λi =λα ei .

5 The unit ball in an infinite-dimensional Hilbert space is not compact in the topology defined by the norm, inwhich fn → f when kfn − f k → 0. Miraculously, the unit ball is compact in another topology, namely the so-called
weak topology, in which fn → f when |(g, fn − f )| → 0 for each fixed g ∈ H.

30

CHAPTER IV. COMPACT OPERATORSThis expansion has the advantage over (IV.2) that it is unique; in (IV.2) there is an arbitrarinessin the choice of basis within the eigenspace of each eigenvalue with multiplicity greater than one.
In particular, the eigenvalues (λi ) are uniquely determined by a.
Corollary IV.9 An arbitrary compact operator a on a Hilbert space H has an expansionXµi (ei , f )ui ,(IV.5)
af =iwhere (ei ) and (ui ) are orthonormal bases of H, µi > 0, and (µ2i ) are the eigenvalues of a∗ a.
Since a∗ a is self-adjoint, Theorem IV.7 applies, i.e.,Xλi (ei , f )ei .
a∗ af =

(IV.6)i

Since a∗ a is positive, in the sense that (f, a√∗ af ) ≥ 0 for all f ∈ H (for (f, a∗ af ) = kaf k2 ), it
follows that λi ≥ 0 for each i. Hence µi = λi is well defined as the positive square-root. Now
define ui := µi−1 aei . Using (III.18), eq. (IV.5) is immediate.
The µi are sometimes called the singular values of a.

Chapter V

Quantum mechanics and Hilbert
space i: states and observables
We are now going to apply the previous machinery to quantum mechanics, referring to the Introduction for history and motivation. The mathematical formalism of quantum mechanics is easierto understand if it is compared with classical mechanics, of which it is a modification. We therefore
start with a rapid overview of the latter, emphasizing its mathematical structure.

V.1

Classical mechanics

The formalism of classical mechanics is based on the notion of a phase space M and timeevolution, going back to Descartes and Newton, and brought into its modern form by Hamilton.
The phase space of a given physical system is a collection of points, each of which is interpreted asa possible state of the system. At each instance of time, a given state is supposed to completely
characterize the ‘state of affairs’ of the system, in that:
1. The value of any observable (i.e., any question that may possibly be asked about the system,
such as the value of its energy, or angular momentum,. . . ) is determined by it.1
2. Together with the equations of motion, the state at t = 0 is the only required ingredient forthe prediction of the future of the system.2
Observables are given by functions f on M . The relationship between states (i.e. points of M )and observables is at follows:
The value of the observable f in the state x is f (x).
This may be reformulated in terms of questions and answers. Here an observable f is identified
with the question: what is the value of f ? A state is then a list of answers to all such questions.
A very basic type of observable is defined by a subset S ⊂ M . This observable is the characteristic function χS of S, given by χS (x) = 1 when x ∈ S and χS (x) = 0 when x ∈/ S. The
corresponding question is: is the system in some state lying in S ⊂ M ? The answer yes is identified
with the value χS = 1 and the answer no corresponds to χS = 0. Sich a question with only two
possible answers is called a yes-no question.
In these notes we only look at the special case M = R2n , which describes a physical system
consisting of a point particles moving in Rn . We use coordinates (q, p) := (q i , pi ), where i =1, . . . , n. The q variable (“position”) denotes the position of the particle, whereas the meaning of
1 Philosophers would say that any quantity pertaining to the system supervenes on its states; this means that nochange in a given quantity is possibly without a change in the state. For example, most scientists would agree thatthe mind supervenes on the brain (seen as a physical system).
2 We do not say that such a prediction is always possible in practice. But if it is possible at all, it merely requires
the state and the equations of motion.

31

32CHAPTER V. QUANTUM MECHANICS AND HILBERT SPACE I: STATES AND OBSERVABLES
the p variable (“momentum”) depends on the time-evolution of the system. For example, for a
free particle of mass m one has the relation p~ = m~v , where v is the velocity of the particle (seebelow). Let us note that one may look at, say, q i also as an observable: seen as a function on M ,one simply has q i (q, p) = q i , etc.
Given the phase space M , the specification of the system is completed by specifying a function
h on M , called the Hamiltonian of the system. For M = R2n we therefore have h as a functionof (q, p), informally written as h = h(q, p). The Hamiltonian plays a dual role:• Regarded as an observable it gives the value of the energy;• it determines the time-evolution of the system.
Indeed, given h the time-evolution is determined by Hamilton’s equations
dq i
dt
dpi
ṗi :=dtq̇ i :=

∂h
;
∂pi
∂h
= − i.
∂q

=

(V.1)

For example, a particle with mass m moving in a potential V has Hamiltonianh(q, p) =p2+ V (q),
2m

(V.2)

Pn
2
i
i
where p2 :=i=1 (pi ) . The equations (V.1) then read q̇ = pi /m and ṗi = −∂V /∂q . With
i
i
2 i
2
the force defined by F := −∂V /∂q , these are precisely Newton’s equations d q /dt = F i /m, or
F~ = m~a. In principle, h may explicitly depend on time as well.

V.2

Quantum mechanics

Quantum mechanics is based on the postulate that the phase space is a Hilbert space H, with theadditional stipulations that:
1. Only vectors of norm 1 correspond to physical states;
2. Vectors differing by a “phase”, i.e., by a complex number of modulus 1, correspond to thesame physical state.
In other word, ψ ∈ H and zψ with z ∈ C and |z| = 1 give the same state.3 We here stick to thephysicists’ convention of denoting elements of Hilbert spaces by Greek letters.4
The reason for the first point lies in the probability interpretation of quantum mechanics. The
simplest example of this interpretation is given by the quantum mechanics of a particle moving in
R3 . In that case the Hilbert space may be taken to be H = L2 (R3 ), and Born and Pauli claimed in
1926 that the meaning of the ‘wavefunction’ ψ ∈ L2 (R3 ) was as follows: the probability P (ψ, x ∈ ∆)that the particle in state ψ is found to be in a region ∆ ⊆ R3 is
Z
P (x ∈ ∆|ψ) = (ψ, χ∆ ψ) =d3 x kψ(x)k2 .
(V.3)∆

Here χ∆ is the characteristic function of ∆, given by χ∆ (x) = 1 when x ∈ ∆ and χ∆ (x) = 0 whenx∈/ ∆. It follows thatP (x ∈ Rn |ψ) = kψk2 = (ψ, ψ) = 1,
(V.4)
3 It follows that the true state space of a quantum-mechanical system is the projective Hilbert space PH, which
may be defined as the quotient SH/ ∼, where SH := {f ∈ H | kf k = 1} and f ∼ g iff f = zg for some z ∈ C with|z| = 1.
4 This notation was initially used by Schrödinger in order to make his wave mechanics, a precursor of quantum
mechanics, look even more mysterious than it already was.

V.3. TRACE-CLASS OPERATORS AND MIXED STATES

33

since by definition of the physical system in question we assume that the particle is somewhere.
More generally, observables are represented in quantum mechanics by self-adjoint operators aon H. In the bounded case (kak < ∞) this means a∗ = a, and the meaning of self-adjointness forunbounded operators will be taken up later. As in classical physics, observables define questions:what is the value of a? And once again, a state ψ ∈ H with kψk = 1 is nothing but a list ofanswers to such questions. Indeed, the answer to the above question in the state ψ is as follows:
The value of the observable a in the state ψ is (ψ, aψ).
Although this is a real number, like in classical physics (thanks to a∗ = a), it now has theinterpretation of the expectation value of the observable a, rather than its exact value.5
In quantum mechanics any projection p defines a so-called yes-no question: is the system insome state in pH? Thus projections are the quantum analogues of characteristic functions inclassical physics. The fundamental difference between classical and quantum physics, namely the
intrinsically statistical character of the latter, is beautifully illustrated by yes-no questions. In theclassical case the possible values of χS are 0 (no) and 1 (yes). In quantum mechanics the answer
to the question p in the state ψ is (ψ, pψ). If ψ ∈ (pH)⊥ one has (ψ, pψ) = 0 and if ψ ∈ pH one
has (ψ, pψ) = 1. Indeed, in the former case (no) one can maintain that the state is not in pH, andin the latter case (yes) one can safely say that the state is in pH. In classical physics these are
the only possibilities: either x ∈ S or x ∈/ S. But in quantum mechanics ψ ∈ (pH)⊥ and ψ ∈ pH
are not the only possibilities! In general, one has the decomposition ψ = ψ k + ψ ⊥ as explained in
Proposition III.6, with both ψ k and ψ ⊥ nonzero. Using p2 = p∗ = p, one finds(ψ, pψ) = (ψ k , pψ k ) ∈ [0, 1];
the fact that (ψ, pψ) ∈ [0, 1] follows from kpk = 1 from projections (which in turn follows from
(III.16)) and (III.5) and Cauchy-Schwarz. Generalizing the interpretation Born and Pauli gave to
wavefunctions, we say that:
The number (ψ, pψ) is the probability that the state ψ lies in pH.
Alternatively:
The probability of the answer yes to the question p in the state ψ is (ψ, pψ).

V.3

Trace-class operators and mixed states

To be honest, we have only discussed a limited class of states so far, namely the pure states.
These are states containing maximum information about the system. Hence in classical physics
pure states are points in phase space, whereas in quantum mechanics they are unit vectors in
Hilbert space. However, in some cases a physics student may not know the precise state of thesystem, for example because he is drunk. A professor of experimental physics may be in the same
situation, this time because he does not know enough theoretical physics to figure out which state
he has prepared in the lab. In either case one is unable to distinguish a number of candidate states.
The way out is to make a list of possible pure states (xk ), xk ∈ M (in classical physics) or (ψk ),ψk ∈ H (in quantum physics
P and assign each state a probability Pk . Of course, one should have
Pk ∈ [0, 1] for each k and k Pk = 1.6 In classical physics the answer to the question what thevalue of f is, is then given byX
hf i =
Pk f (xk ).
kIn quantum physics, the value of a in the given state ρ isXhai =Pk (ψk , aψk ).

(V.5)

k

5 What this really means is a matter of ongoing debate. For example, do expectation values refer to outcomes ofa long series of measurements? Or to ‘propensities’ of the observable to have the given expectation value? Or toaverages with respect to some unknown theory underlying quantum mechanics? EtceteraR
6 More generally, one take a probability measure µ on M and takes the value of f in the state µ to bedµ f .
M

34CHAPTER V. QUANTUM MECHANICS AND HILBERT SPACE I: STATES AND OBSERVABLES
In textbooks on quantum mechanics one here introduces a density matrixXρ=
Pk [ψk ],

(V.6)

k

where [ψk ] is the projection onto the one-dimensional subspace spanned by ψk , i.e. (for generalunit vectors ψ)[ψ]ϕ := (ψ, ϕ)ψ;(V.7)
this is the one-dimensional case of (III.18). Note that physicists write [ψ] = |ψihψ| as well as |ϕiinstead of ϕ; eq. (III.18) then reads|ψihψ|ϕi = |ψihψ|ϕi.
This a tautology; it coincides with (III.18) since hψ|ϕi = (ψ, ϕ). This is one of the few instanceswhere the so-called Dirac notation used in quantum mechanics is actually better than the one inmath books.
In any case, one now writes (V.5) as
hai = Tr (ρa).

(V.8)

Here the trace of a (bounded) operator b is defined as follows: take any orthonormal basis (ei ) of
H and putX(ei , bei ).
(V.9)
Tr b :=i

This is supposed to be independent of the chosen basis. So if ρ is given by (V.6) and the vectors
ψk are mutually orthogonal, one may choose a basis that contains all of them. In that case one
easily shows thatX
Tr (ρa) =Pk (ψk , aψk ),(V.10)
kso that (V.9) and (V.5) are the same.
In quantum mechanics density matrices are often called mixed states. If (V.6) contains only
one term ρ = [ψ], one has Tr (ρa) = (Ψ, aψ) and the state is actually “pure”.
We now present the general theory of the trace; this is more complicated than the physics
literature suggests. For example, the independence of the trace on the basis chosen is delicate. Ifdim(H) < ∞ there is no problem; a simple computation shows thatXX
(ui , aui )(ei , aei ) =iifor any two o.n.b. (ei ) and (ui ) (just expand ui in terms of the ei and interchange summations).
However, if dim(H)P= ∞ one can find bounded
operators b : H → H and orthonormal bases (uk )P
and (ei ) such that i (ei , bei ) = ∞ and k (uk , bek ) = ∞ (exercise).
To avoid this, in order to make sense of the trace when H is infinite-dimensional one needs aclass of operators a for which (V.9) is i) finite and ii) independent of the basis. This class can beconstructed in (at least) two different but equivalent ways. The simplest way is as follows.
1. In order to have a well-defined trace, an operator b needs to be bounded and compact.
2. If b is compact, then so is b∗ b (exercise). Morever, b∗ b is self-adjoint, since(b∗ b)∗ = b∗ b∗∗ = b∗ b.
3. So we can apply Theorem IV.7 with a = b∗ b. This yields
Xb∗ b =µk pk
kfor certain µk ∈ R and projection pk : H → H. Now, if ψ ∈ pk H, so that b∗ bψ = µk ψ, taking
the inner product with ψ one has (ψ, b∗ bψ) = µk (ψ, ψ), i.e. kbψk2 = µk kψk2 . Hence µk ≥ 0.

V.3. TRACE-CLASS OPERATORS AND MIXED STATES

35

Definition
P √ V.1 We say that a bounded operator b : H → H is trace-class when b is compact
and k µk < ∞. Here the µk are the eigenvalues of b∗ b. If b is trace-class we write b ∈ B1 (H).

Another approach to the trace is based on the notion of the square root of the operator b∗ bitself (instead of on the square root of its eigenvalues, as above). Let us call an operator b : H → H
positive when (f, bf ) ≥ 0 for all f ∈ H. We write b ≥ 0. We will often use the simple property
that if b is positive, then b∗ = b. It turns out that if b is positive, then (V.9) is independent of theorthonormal basis (ei ); so it is either finite or infinite. If it is finite this defines the trace of b, andwe say b is trace-class. This defines the trace for positive operators.
To define the trace in general, one uses the square-root of a positive operator.
Namely, one√
can show (exercise) that if b is positive, then there exists a unique operator b with the properties√√ 2
b = b en b ≥ 0. In particular, the absolute value√(V.11)|b| := b∗ b
is defined, as we have just seen that b∗ b is positive. We now say that an arbitrary bounded operator
b is trace-class if Tr |b| < ∞, where the trace is given by (V.9). It turns out that b is trace-class inthis sense iff it is trace-class according to Definition V.1.
In any case, the most important properties of the trace are:
Theorem V.2 Suppose b is trace-class, or b ∈ B1 (H). Then:
1. The expression (V.9), called the trace of b, is absolutely convergent and independent of theorthonormal basis (ei ).
2. If a is bounded, then ab ∈ B1 (H) and ba ∈ B1 (H) and3. If u is unitary thenTr (ab) = Tr (ba).

(V.12)Tr (ubu−1 ) = Tr (b).

(V.13)

This theorem is easy to use but by no means easy to prove, so we leave this as a special project.
Instead, we return to quantum mechanics.
Definition V.3 An bounded operator ρ : H → H is called a density operator or density
matrix if:
1. ρ is positive;
2. ρ is trace-class;
3. Tr ρ = 1.
By part 2 of Theorem V.2, for any bounded operator a the expectation value (V.8) is well defined.
Furthermore, since ρ is compact (cf. Definition V.1) and self-adjoint (as follows from its positivity),
we may apply Theorem IV.7 with a = ρ. This yields the expansion (V.6) and we have come fullcircle.
The above mathematical model of quantum mechanics is too limited, however. The states
are OK, but most observables in quantum mechanics turn out to be unbounded. Position andmomentum are examples. “It is a fact of life that many of the most important operators which
occur in mathematical physics are not bounded” [18]. We now turn to the theory of such unbounded
observables.

36CHAPTER V. QUANTUM MECHANICS AND HILBERT SPACE I: STATES AND OBSERVABLES

Chapter VI

Closed unbounded operators
VI.1

The closure

We are now familiar with two classes of operators on a Hilbert space: bounded ones, and compact
ones. As we have seen, the latter are precisely those bounded operators for which a spectral theory
analogous to that for matrices exists. It would be possible to develop the spectral theory of generalbounded (self-adjoint)operators at this point, but in fact it turns out that this theory can be
written down almost without extra effort for the far wider class of closed (self-adjoint) operators.
To come to grips with the notion of un unbounded operator, we note the following.
Theorem VI.1 An operator on a Hilbert space H is bounded iff it is continuous in the sense that
fn → f implies afn → af for all convergent sequences (fn ) in H.
Let a : H → H be bounded. By (III.5), if fn → f in H, that is, kfn − f k → 0, then afn → af inH, since kafn − af k ≤ kakkfn − f k. Conversely, if a is not bounded, then for each n ∈ N there
is fn ∈ H with kfn k = 1 and kafn k ≥ n. The sequence (gn = fn /n) converges to 0, but sincekagn k ≥ 1, the sequence (agn ) does not converge to a0 = 0. Hence a is not continuous, and theimplication “continuous ⇒ bounded” has been proved by reductio ad absurdum.
It follows that unbounded operators are discontinuous: if fn → f it is not guaranteed that
afn → af ; indeed, it is not guaranteed that (afn ) converges at all! Hence an essential difference
between the bounded and the unbounded case is that whereas for bounded operators a Proposition
III.3 states that even if a is initially defined on some dense subspace D of H, it can uniquely be
extended to H by continuity, for unbounded operators a such an extension by continuity will not ingeneral exist. Although a discontinuous extension to H might exist, in practice it is very unusual totalk about unbounded operators a defined on H. This is because interesting unbounded operators
tend to be defined on D ⊂ H by some natural expression, which simply does not make sense on allof H. Consequently, the specification of the subspace D on which a is defined, called the domainof a, denoted by D(a) is absolutely essential when a is an unbounded operator.
Recall that a subspace K ⊂ H is called dense in H when for each f ∈ H there is a sequence(fn ) in K converging to f .
Unless explicitly stated otherwise, we always assume that the domain of an operator isdense in H.
For example, we have seen that a ∈ C(R) defines a bounded operator on H = L2 (R) by
multiplication when kf k∞ < ∞, i.e., when a is bounded. When a is unbounded as a function, we
cannot really make the natural claim that consequently a is unbounded as a multiplication operator
on H, since a does not map H to itself as soon as it is unbounded. What we can say, however, isthat a is unbounded as a multiplication operator on the subspace D(a) = Cc (R), which (by ourdefinition of L2 ) is dense in H. Other examples are given by differential operators: whereas one
cannnot really say that a = id/dx is unbounded on L2 (R) since the derivative of most functions in
L2 is not even defined, one can say that a is unbounded on, say, D(a) = Cc1 (R) := Cc (R) ∩ C 1 (R).
37

38

CHAPTER VI. CLOSED UNBOUNDED OPERATORS

There is some flexibility in the choice of the domain, and the theory of unbounded operatorsof closable type even largely revolves around the business of enlarging a given domain. Here
we define an extension of a given operator a : D(a) → H as an operator a1 : D(a1 ) → H
where D(a) ⊂ D(a1 ) and a1 = a on D(a); we write a ⊂ a1 . As we have seen, if a is bounded
there is only one interesting extension, namely the one defined on any larger domain (includingH) by continuity, but in the unbounded case the theory of extension of operators turns out tobe very rich indeed. As a rather trivial example, we could have defined a = id/dx initiallyon D(a) = Cc∞ (R) := Cc (R) ∩ C ∞ (R); the same expression a1 = id/dx but now defined on
D(a1 ) = Cc1 (R) is formally an extension of a. Similarly, an unbounded mutliplication operatora ∈ C(R) may initially be defined on D(a) = Cc∞ (R), to be extended by the same expression
defined on D(a1 ) = Cc (R), as above. Clearly, the largest possible domain on which a multiplication
operator a ∈ C(Ω) can be defined in the natural way isD(a) = {f ∈ L2 (Ω) | af ∈ L2 (Ω)}.

(VI.1)The particular unbounded operators that one can deal with satisfy a relic of continuity. To
explain this, we define the graph of a as the subset {f, af } ⊂ H × H. To define the notionof closedness in H × H we equip this set with the structure of a Hilbert space in the following
way: we first equip H × H with the structure of a vector space by defining hf1 , f2 i + hg1 , g2 i :=hf1 + g1 , f2 + g2 i and λhf1 , f2 i := hλf1 , λf2 i, and subsequently putting an inner product on it by(hf1 , f2 i, hg1 , g2 i) := (f1 , g1 ) + (f2 , g2 ). With this structure, H × H is a Hilbert space called H ⊕ H,
for it is precisely the direct sum of H with itself.
Definition VI.2 A closed operator a : D(a) → H is a linear map from a dense subspace
D(a) ⊂ H to H for which either of the following equivalent conditions holds:• If fn → f in H for a sequence (fn ) in D(a) and (afn ) converges in H, then f ∈ D(a) and
afn → af in H.
• The graph of a is closed.
• The domain D(a) is closed in the norm kf k2a := kf k2 + kaf k2 .
Note that the norm k · ka comes from the new inner product (f, g)a := (f, g) + (af, ag) on D(a).
Hence D(a) is a Hilbert space in the new inner product when a is closed.
It is quite unusual for an operator to be closed in the form initially given. For example, amultiplication operator a in L2 (Ω) is not closed on Cc (Ω); in fact, a turns out to be closed only
on the domein (VI.1). Fortunately, an operator a that is not closed may often be extended into a
closed one. The condition for this to be possible is as follows.
Definition VI.3 A closable operator a : D(a) → H is a linear map from a dense subspace
D(a) ⊂ H to H with the property that the closure G(a)− of its graph is itself the graph G(a− ) of
some operator a− (called the closure of a).
In other words, a− is a closed extension of a. It is clear that a− is uniquely defined by its graph
G(a− ) = G(a)− .
Proposition VI.4 An operator a is closable iff either of the following equivalent conditions holds:• If fn → 0 for a sequence (fn ) in D(a) and if (afn ) converges, then (afn ) must converge to 0.
• The closure G(a)− of the graph of a does not contain any element of the form (0, g) for g 6= 0.
In that case, the domain of the closure a− of a is the set of all f ∈ H to which some sequence (fn )in D(a) converges and for which (afn ) converges as well. Its action is given by a− f := limn afn .
Finally, the operator a− is the smallest closed extension of a.

VI.2. SYMMETRIC AND SELF-ADJOINT OPERATORS

39

To verify that a− is indeed closed, suppose fn → f and afn → g, with (fn ) in D(a− ). Since
fn ∈ D(a− ) for fixed n, there exists (fm,n ) in D(a) such that limm fm,n = fn and limm afm,n =: gnexists. Then clearly limm,n fm,n = f , and we claim that
lim afm,n = g.

m,n(VI.2)

Namely, kafm,n −gk ≤ kafm,n −afn k+kafn −gk. For ǫ > 0 take n so that the second term is < ǫ/2.
For that fixed n, a(fm,n − fn ) converges as m → ∞ because afm,n → gn and afn is independent ofm. Also, recall that fm,n − fn → 0 as m → ∞. By assumption, a is closable, hence by definition
one must have a(fm,n − fn ) → 0 in m. Hence we may find m so that kafm,n − afn k < ǫ/2, so that
kafm,n − gk < ǫ, and (VI.2) follows. Hence f ∈ D(a− ). Finally, since a− f := limm,n afm,n one
has a− f = g by (VI.2), or a− f = limn afn by definition of g. It follows that a− is closed. Thisextension of a is clearly the minimal closed one.
This argument shows that for a− to be closed it is sufficient that a is closable. Conversely, if a
fails to be closable it cannot have any closed extension whatsoever, since a− is by definition linear,and a− 0 = 0 for any operator. The second condition for closability is then clearly equivalent to
the first.

For example, a multiplication operator a in H = L2 (Ω) is closable on Cc (Ω): in both cases theclosure has domain (VI.1). On the other hand, an example of a non-closable operator is given by
H = L2 (Ω), D(a) = Cc (Ω), and af := f (0)g, where g ∈ H is arbitrary.
In general, even closed unbounded operators may have closed extensions, a phenomenon whichis particular important in connection with the theory of self-adjointness; see below.
This section would not be complete without mentioning a special case of one of the most famous
theorems in functional analysis, namely the closed graph theorem (for Hilbert spaces):
Theorem VI.5 If a : H → H has a closed graph, then it is bounded. In other words, a closed
operator defined on all of H is necessarily bounded.
We will use this theorem only once, since in applications an operator defined on H is usually
already known to be bounded, whereas unbounded operators are never defined on H, so that theirgraph cannot be studied in any case.1

VI.2

Symmetric and self-adjoint operators

The most important closed operators, not just for quantum mechanics also but for mathematics
as a whole, are the self-adjoint ones. To define these, first recall the definition of the adjoint a∗ inthe unbounded case: see Definition III.4.
Although it may even happen that D(a∗ ) is zero, this is pretty pathological, and in most‘natural’ examples D(a∗ ) turns out to be dense. For example, a multiplication operator a ∈ C(Ω) on
H = L2 (Ω), defined on any of the domains we have considered (i.e., D(a) = Cc∞ (Ω), D(a) = Cc (Ω),
or (III.17)), has a∗ = a (i.e., the complex conjugate of a seen as a multiplication operator) definedon D(a∗ ) given by the right-hand side of (III.17). The following important result shows whenD(a∗ ) is dense.
Proposition VI.6 Let a : D(a) → H (where D(a) is dense) be an operator on a Hilbert space H.
1. The adjoint a∗ is closed.
2. The operator a is closable iff D(a∗ ) is dense.
3. In that case, one has a− = a∗∗ and (a− )∗ = a∗ .
1 On the theoretical side, however, an important consequence of the closed graph theorem is another famous
result known as the open mapping theorem: a bounded surjective operator on a Hilbert space H is open (in thatthe image of an open set in H is open; the boundedness of H implies the opposite, namely that the inverse imageof an open set in H is open).

40

CHAPTER VI. CLOSED UNBOUNDED OPERATORSThe proof can be elegantly given in terms of the graph G(a). Defining U : H ⊕ H → H ⊕ H by
U (f, g) := (g, −f ), it is easy to verify that (exercise!)G(a∗ ) = U (G(a)⊥ ) = U (G(a))⊥ .

(VI.3)

Hence the first claim immediately follows, since any orthogonal complement is closed (cf. Lemma
II.10).
By Proposition (II.10) and (VI.3) one has G(a)− = G(a)⊥⊥ = (U −1 G(a∗ ))⊥ . But(U −1 G(a∗ ))⊥ = {hf, gi ∈ H ⊕ H | (f, a∗ h) − (g, h) = 0 ∀h ∈ D(a∗ )}.
Consequently, referring to Proposition VI.4, (0, g) ∈ G(a)− iff g ∈ D(a∗ )⊥ , proving the second
claim. Using (VI.3) twice (first for a∗ , then for a), we haveG(a∗∗ ) = (U G(a∗ ))⊥ = (U 2 (G(a)⊥ ))⊥ = G(a)⊥⊥ = G(a)− = G(a− ),where we have used U 2 = −1 and −G(b) = G(b) for any operator b. This proves the first part of
the third claim. Finally, if a is closable, then using (VI.3) and Lemma II.10 and (VI.3) once more,
we haveG(a∗ ) = U (G(a)⊥ ) = U ((G(a)− )⊥ ) = U (G(a− )⊥ ) = G((a− )∗ ).

We now come to the central definition of this chapter. An equality a = b between unbounded
operators always stand for D(a) = D(b) and a = b. Similarly, a ⊂ b means D(a) ⊂ D(b) and b = a
on D(a).
Definition VI.7 Let a : D(a) → H (where D(a) is dense) be an operator on a Hilbert space H.
• If a = a∗ , i.e., if D(a∗ ) = D(a) and (af, g) = (f, ag) for all f, g ∈ D(a), then a is called
self-adjoint.
• If a∗∗ = a∗ (equivalently, if a is closable and a− = a∗ or (a− )∗ = a∗ ), then a is called
essentially self-adjoint.
• If a ⊂ a∗ i.e., if (af, g) = (f, ag) for all f, g ∈ D(a), then a is called symmetric.
It follows Proposition VI.6 that a self-adjoint operator is closed, and that a symmetric operator
is closable (because D(a∗ ), containing D(a), is dense). For a symmetric operator one has a ⊆a− = a∗∗ ⊆ a∗ , with equality at the first position when a is closed, and equality at the second
position when a is essentially self-adjoint; when both equalities hold, a is self-adjoint. Conversely,
an essentially self-adjoint operator is, of course, symmetric. A symmetric operator may or may
not have self-adjoint extensions; we will deal with this problem in detail later on. Without proof
we quote the Hellinger–Toeplitz theorem: If a is self-adjoint on D(a) = H, then a is bounded.
This confirms the idea that it is pointless to try to define unbounded operators on all of H in somemanifestly discontinuous way. All this is very subtle, but the following example illustrates at least
the easy part of the theory:
Proposition VI.8 A real-valued multiplication operator a ∈ C(Ω) on H = L2 (Ω) is essentiallyself-adjoint on D(a) = Cc∞ (Ω) and on D(a) = Cc (Ω), and is self-adjoint on
D(a) = {f ∈ L2 (Ω) | af ∈ L2 (Ω)}.

(VI.4)

Cf. (III.17)). Of course, D(a) = H when kak∞ < ∞, since in that case a is a bounded operator,
as we have seen.
The proof is Ralmost trivial: by definition, D(a∗ ) consists of all f ∈ L2 (Ω) for wich the functional
g 7→ (f, ag) = Ω dn x f (x)a(x)g(x) is bounded; for each of the three choices of D(a) listed this
implies f a ∈ L2 (Ω) by Riesz–Fréchet, so that D(a∗ ) = {f ∈ L2 (Ω) | af ∈ L2 (Ω)}, with a∗ = a asa multiplication operator. If a is real-valued, then a∗ is self-adjoint by the very same argument,
which implies all claims.
Chapter VII

Spectral theory for selfadjoint
operators
We denote the kernel or null space of a map a by N (a) and its range or image by R(a). As before,
D(a) denotes the domain of a. Also, a − z for z ∈ C denotes the operator a − z1.

VII.1

Resolvent and spectrum

The theory of the spectrum of a closed operator on a Hilbert space (which may be bounded or
unbounded) is a generalization of the theory of eigenvalues of a matrix. From linear algebra werecall:Proposition VII.1 Let a : Cn → Cn be a linear map. The a is injective iff it is surjective.
This follows from the fundamental fact that if a : V → W is a lnear map between vector spaces,
one has R(a) ∼= V /N (a). If V = W = Cn , one one count dimensions to infer that dim(R(a)) =n − dim(N (a)). Surjectivity of a yields dim(R(a)) = n, hence dim(N (a)) = 0, hence N (a) = 0,
and vice versa.1
Corollary VII.2 Let a : Cn → Cn be a linear map. Then a − z is invertible (i.e., injective andsurjective) iff z is not an eigenvalue of a, i.e., if there exists no f ∈ Cn such that af = zf .
Defining the spectrum σ(a) of a : Cn → Cn as the set of eigenvalues of a and the resolvent
ρ(a) as the set of all z ∈ C for which a − z is invertible, we therefore haveσ(a) = C\ρ(a).

(VII.1)

If z ∈ ρ(a), the equation (a − z)f = g for the unknown f ∈ Cn has a unique solution for any g;existence follows from the surjectivity of a − z, whereas uniqueness follows from its injectivity (ifa − z fails to be injective then any element of its kernel can be added to a given solution).
Now, if a is an operator on an infinite-dimensional Hilbert space, it may not have any eigenvalues, even when it is bounded and self-adjoint. For example, if a(x) = exp(−x2 ) the associatedmultiplication operator a : L2 (R) → L2 (R) is bounded and self-adjoint, but it has no eigenvalues at
all: the equation af = λf for eigenvectors is exp(−x2 )f (x) = λf (x) for (almost) all x ∈ R, whichholds only if f is nonzero at a single point. But in that case f = 0 as an element of L2 . However,the situation is not hopeless. More generally, let any a ∈ Cb (R), interpreted as a multiplication
1 In general, this proposition yields the very simplest case of the Atiyah–Singer index theorem, for which theseauthors received the Abel Prize in 2004. We define the index of a linear map a : V → W as index(a) :=dim(ker(a)) − dim(coker(a)), where ker(a) = N (a) and coker(a) := W/R(a), provided both quantities are finite. If
V and W are finite-dimensional, Proposition VII.1 yields index(a) = dim(V ) − dim(W ); in particular, if V = W
then index(a) = 0 for any linear map a. In general, the index theorem expresses the index of an operator in termsof topological data; in this simple case the only such data are the dimensions of V and W .

41

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CHAPTER VII. SPECTRAL THEORY FOR SELFADJOINT OPERATORS

operator a : L2 (R) → L2 (R). If x0 ∈ R one may find approximate eigenvectors of a in the following
sense: take2
fn (x) := (n/π)1/4 e−n(x−x0 ) /2 .
(VII.2)
Then kfn k = 1 and limn→∞ (a(x) − a(x0 ))fn = 0, although the sequence fn itself has no limit in
L2 (R). Thus we may call λ = a(x0 ) something like a generalized eigenvalue of a for any x0 ∈ R,
and define the spectrum accordingly: let a : D(a) → H be a (possibly unbounded) operator on a
Hilbert space. We say that λ ∈ σ(a) when there exists a sequence (fn ) in D(a) for which kfn k = 1
andlim (a − λ)fn = 0.
(VII.3)n→∞

Of course, when λ is an eigenvalue of a with eigenvector f , we may take fn = f for all n.
However, this is not the official definition of the spectrum, which is as follows.
Definition VII.3 Let a : D(a) → H be a (possibly unbounded) operator on a Hilbert space. The
resolvent ρ(a) is the set of all z ∈ C for which a − z : D(a) → H is injective and surjective (i.e.,
invertible). The spectrum σ(a) of a is defined by σ(a) := C\ρ(a).
Hence the property (VII.1) has been turned into a definition! We will prove the equivalenceof this definition of the spectrum with the definition above later on. In the example just given,
one has σ(a) = a(R) if the right domain of a is used, namely (III.17). Thus the spectrum canbe nonempty even if there aren’t any eigenvalues. The subsequent theory shows that these are
precisely the right definitons for spectral theory.
The following result explains the role of closedness.2
Proposition VII.4 If an operator a : D(a) → R(a) = H has an inverse, then a−1 is bounded iff
a is closed.
The proof consists of two steps. First, one has that a : D(a) → R(a) is closed iff a−1 is closed.
To prove “⇒”, assume gn → g and a−1 gn → f . Call fn := a−1 gn ; then afn = gn → g, so if a
is closed then by definition f ∈ D(a) and afn → af , so af = g, hence f = a−1 g, which means
a−1 gn → a−1 g. In particular, g ∈ R(a) = D(a−1 ), and it follows that a−1 is closed. The proof of“⇐” is the same, with a and a−1 interchanged. Geometrically, the graph of a−1 is just the imageof the graph of a in H ⊕ H under the map (f, g) 7→ (g, f ), hence if one is closed then so is theother.
Secondly, if R(a) = H, then D(a) = H, hence a−1 is bounded by the closed graph theorem(Theorem VI.5).

Returning to the equation (a − z)f = g, it now follows that when z ∈ ρ(a), the solution
f depends continuously on the initial data g iff a is closed. To avoid pathologies, we therefore
assume that a is closed in what follows. Furthermore, as we shall see, practically every argument
below breaks down when (a−z)−1 is unbounded. This also explains why as far as spectral theory isconcerned there isn’t much difference between bounded operators and closed unbounded operators:
in both cases (a − z)−1 is bounded for z ∈ ρ(a).
As an exercise, one easily shows:
Proposition VII.5 Let a be a closed operator.
1. ρ(a) is open (and hence σ(a) is closed) in C.
2. ρ(a∗ ) = ρ(a);σ(a∗ ) = σ(a).

For unbounded operators the spectrum can (literally) be any subset of C, including the empty
set.
2 Some books define the resolvent of a as the set of those z ∈ C for which (a − z) is invertible and has bounded
inverse. In that case, the resolvent is empty when a is not closed.

VII.2. THE SPECTRUM OF SELF-ADJOINT OPERATORS

VII.2

43

The spectrum of self-adjoint operators

For a general closed operator a, we may decompose the spectrum asσ(a) = σd (a) ∪ σc (a),(VII.4)

where the discrete spectrum σd (a) consists of all eigenvalues of a, and the continuous spectrum σc (a) is the remainder of σ(a). Recall that eigenvalues lie in σ(a), for if (a−λ)f = 0 for somenonzero f then a − λ cannot be injective. The spectrum of self-adjoint operators has a particularlytransparent structure.
Theorem VII.6 Let a be a self-adjoint operator (i.e., a∗ = a), and let z ∈ C. Then one of thefollowing possibilities occurs:
1. R(a − z) = H iff z ∈ ρ(a);2. R(a − z)− = H but R(a − z) 6= H iff z ∈ σc (a);3. R(a − z)− 6= H iff z ∈ σd (a).
The key to the proof is a very simple result.
Lemma VII.7 If a is closable (equivalently, if D(a∗ ) is dense), then R(a − z)− = N (a∗ − z)⊥
and N (a∗ − z) = R(a − z)⊥ .
Note that the kernel of a closed operator (in this case a∗ − z) is automatically closed. Easy
calculations using the definition of a∗ yield the inclusions R(a − z)⊥ ⊂ N (a∗ − z) and R(a − z) ⊂N (a∗ − z)⊥ . Since K ⊥⊥ = K − for any linear subspace K of a Hilbert space, and K ⊂ L impliesL⊥ ⊂ K ⊥ , the claim follows.

We first prove Theorem VII.6 for z ∈ R. If R(a − z)− 6= H, then N (a − z) = R(a − z)⊥ 6= 0, so(a − λ)f = 0 has a nonzero solution and λ ∈ σd (a). The converse implication has the same proof.
If R(a − z)− = H, then N (a − z) = 0 and a − z is injective. Now if R(a − z) = H then a − z
is surjective as well, and z ∈ ρ(a). The converse is trivial given the definition of the resolvent. If
R(a − z) 6= H, then z ∈ σc (a) by definition of the continuous spectrum. Conversely, if z ∈ σc (a)then z ∈/ σd (a) and z ∈/ ρ(a), so that R(a − z) = H and R(a − z)− 6= H are exlcuded by theprevious ‘iff’ results for ρ(a) and σd (a). Hence R(a − z)− = H but R(a − z) 6= H.
To prove Theorem VII.6 for z ∈ C\R, we first note that eigenvalues of self-adjoint operators
must be real; this is immediate since if a∗ = a then (f, af ) = (af, f ) = (f, af ), so if f is aneigenvector with eigenvector λ it follows that λ = λ. In fact, we will prove that if z ∈ C\R, thenalso z ∈ ρc (a) is impossible, so that z ∈ ρ(a). To see this we need some lemma’s.
Lemma VII.8 Let a be symmetric. Then k(a − z)f k ≥ |Im (z)|kf k.
Reading Cauchy–Schwarz in the wrong order, we obtaink(a − z)f kkf k ≥ |(f, (a − z)f )| = |(r − iIm (z))kf k2 | ≥ |Im (z)|kf k2 .
Here we used the fact that r := (f, af ) − Re (z) is a real number by virtue of the symmetry ofa.
Hence z ∈ C\R implies N (a − z) = 0. Combining this with Lemma VII.7, we infer thatR(a − z)− = N (a − z)⊥ = H. To infer that actually R(a − z) = H we need yet another lemma.
Lemma VII.9 Let a be any densely defined operator. If kaf k ≥ Ckf k for some C > 0 and allf ∈ D(a), then a is injective and a−1 : R(a) → D(a) is bounded with bound ka−1 k ≤ C −1 .

44

CHAPTER VII. SPECTRAL THEORY FOR SELFADJOINT OPERATORS

Injectivity is trivial, for af = 0 cannot have any nonzero solutions given the bound; a linear map
a is injective when af = 0 implies f = 0. For the second claim, note thatka−1 k = sup{ka−1 gk, g ∈ D(a−1 ) = R(a), kgk = 1} =




f
−1 af
, f ∈ D(a), f 6= 0 = sup, f ∈ D(a), f 6= 0 .
sup a
kaf k
kaf k

This yields the claim.

Combining this with Lemma VII.8, we see that z ∈ C\R implies (a − z)−1 : D((a − z)−1 ) →D(a − z) = D(a) is bounded, where D((a − z)−1 ) = R(a − z). To infer that in fact R(a − z) = H,
we use:Lemma VII.10 If b is closed and injective, then b−1 : R(b) → D(b) is closed.
See the proof of Proposition VII.4.



Lemma VII.11 If b is closed and bounded, then D(b) is closed.
This is immediate from the definition of closedness.

Taking b = (a − z)−1 , we find that D((a − z)−1 ) is closed. Since we know that R(a − z)− = H,
we conclude that R(a − z) = H. The same is true for z. Hence by Lemma VII.7, N (a − z) =R(a − z)⊥ = H ⊥ = 0 and a − z is injective. With a − z already known to be surjective, z ∈ ρ(a).
The proof of the converse implications is the same as for z ∈ R, and we have finished the proofof Theorem VII.6.

Using similar arguments, one can prove
Theorem VII.12 Let a be a symmetric operator. Then the following properties are equivalent:
1. a∗ = a, i.e., a is self-adjoint;
2. a is closed and N (a∗ ± i) = 0;
3. R(a ± i) = H;
4. R(a − z) = H for all z ∈ C\R;5. σ(a) ⊂ R.
Similarly, the following properties are equivalent:
1. a∗ = a∗∗ , i.e., a is essentially self-adjoint;
2. N (a∗ ± i) = 0;
3. R(a ± i)− = H;
4. R(a − z)− = H for all z ∈ C\R;5. σ(a− ) ⊂ R.
The second half of the theorem easily follows from the first, on which we will therefore concentrate.
The implications 1 ⇒ 2, 1 ⇒ 4, 1 ⇒ 5 and 2 ⇒ 3 are immediate either from Theorem VII.6 orfrom its proof. The implications 4 ⇒ 3 and 5 ⇒ 4 are trivial. Thus it only remains to prove 3 ⇒ 1.
To do so, assume R(a ± i) = H. For given f ∈ D(a∗ ) there must then be a g ∈ H suchthat (a∗ − i)f = (a − i)g. Since a is symmetric, we have D(a) ⊂ D(a∗ ), so f − g ∈ D(a∗ ), and(a∗ − i)(f − g) = 0. But N (a∗ − i) = R(a + i)⊥ by Lemma VII.7, so N (a∗ − i) = 0. Hence
f = g, and in particular f ∈ D(a) and hence D(a∗ ) ⊂ D(a). Since we already know the opposite
inclusion, we have D(a∗ ) = D(a). Given symmetry, this implies a∗ = a.
VII.3. APPLICATION TO QUANTUM MECHANICS

45

Corollary VII.13 Let a∗ = a. The σ(a) ⊂ R. In other words, the spectrum of a self-adjoint
operator is real.
As an illustration of Theorem VII.12, one can directly show:
Proposition VII.14 Let a ∈ C(Ω) define a real-valued multiplication operator on
D(a) = {f ∈ L2 (Ω) | af ∈ L2 (Ω)} ⊂ H = L2 (Ω),

so that a∗ = a (cf. Proposition VI.8.) Then the operator a is injective iff a(x) 6= 0 for all x ∈ Ω,and surjective iff there exists ε > 0 so that |a(x)| ≥ ε for all x ∈ Ω; in that case a is injective andhas bounded inverse. Consequently, σ(a) = a(Ω)− , with a(Ω) := {a(x), x ∈ Ω}.
Finally, we justify our earlier heuristic definition of the spectrum; the thrust of the theorem
lies in its characterization of the continuous spectrum, of course.
Theorem VII.15 Let a be self-adjoint. Then λ ∈ σ(a) iff there exists a sequence (fn ) in D(a)with kfn k = 1 for all n such that limn (a − λ)fn = 0.
Suppose λ ∈ σ(a). If λ ∈ σd (a) we are ready, taking fn = f for all n. If λ ∈ σc (a), thenR(a − λ)− = H but R(a − λ) 6= H by Theorem VII.6. Now a is self-adjoint, hence a and a − λ areclosed, so that also (a−λ)−1 is closed by Lemma VII.10. Hence (a−λ)−1 : R(a−λ) → H must be a adensely defined unbounded operator by Lemma VII.11, for if it were bounded then its domain would
be closed, which D((a − λ)−1 ) = R(a − λ) is not, as we have just shown. Thus there is a sequence
gn in D((a − λ)−1 ) with norm 1 and k(a − λ)−1 gn k → ∞. Then fn := (a − λ)−1 gn /k(a − λ)−1 gn k
has the desired property.
Conversely, if λ ∈ ρ(a) then (a − λ)−1 is bounded, hence (a − λ)fn → 0 implies fn → 0, so thesequence (fn ) cannot exist, and λ ∈ σ(a) by reductio ad absurdum.


VII.3

Application to quantum mechanics

The theory of self-adjoint operators has many applications, for example to the theory of boundary
value problems for linear partial differential equations. In these notes we focus on applications toquantum mechanics.
In Chapter V we initially assumed that observables in quantum mechanics are mathematically
represented by bounded self-adjoint operators, i.e. linear maps a : B(H) → B(H) such thatkak < ∞ and a∗ = a. As already mentioned at the end of that chapter, however, this model is too
limited. For example, in physics textbooks you will find the position and momentum operators
q̂ i

=

xi

p̂i

=

−i~

∂
.
∂xi

(VII.5)Here ~ ∈ R+ is a constant of nature, called Planck’s constant, and i = 1, 2, 3. These operators
are allegedly defined on H = L2 (R3 ), but we know from the previous chapter that at least q̂ i is
unbounded. It is a multiplication operator of the form aψ(x) = âψ(x) with â ∈ C(R3 ), in thiscase â(x) = xi . As we have seen, a is bounded iff kâk∞ < ∞, and this clearly not the case for xi .
Hence the position operator is unbounded. It follows from Proposition VI.8 that q̂ i is self-adjoint
on the domain
D(q̂ i ) = {ψ ∈ L2 (R3 ) | xi ψ ∈ L2 (R3 )},(VII.6)

where xi ψ is shorthand for the function x 7→ xi ψ(x).
Although we have not had the opportunity to develop the necessary machinery, the story of
the momentum operator p̂i is similar. If we denote the Fourier transform of ψ ∈ L2 (R3 ) by ψ̂, andcall its argument k = (k1 , k2 , k3 ) ∈ R3 , we can writeZ
d3 x ψ(x)e−ikx ,(VII.7)ψ̂(k) =
R3

1

2

3

where kx = x k1 + x k2 + x k3 . This inproper integral is defined as follows.

46

CHAPTER VII. SPECTRAL THEORY FOR SELFADJOINT OPERATORS
1. Approximate ψ by a sequence (ψn ) in Cc (R3 ) (i.e. limn→∞ ψn = ψ in the norm of L2 (R3 )).
This is possible by Proposition II.9.
2. Putψ̂n (k) :=Z

d3 x ψn (x)e−ikx ,(VII.8)R3

as proper Riemann integrals (the integrand is continuous and since ψn has compact support
the integral is proper, i.e. over a finite region).
3. It turns out that (ψ̂n ) is a Cauchy sequence in L2 (R3 ), so we may finally defineψ̂ := lim ψ̂n ,n→∞(VII.9)where the limit is taken in L2 (R3 )It turns out that ψ ∈ L2 (R3 ) iff ψ̂ ∈ L2 (R3 ), and that one can reconstruct ψ from ψ̂ byZ
d3 k
ψ̂(k)eikx ,(VII.10)
ψ(x) =
3
R3 (2π)which is defined by the same procedure as (VII.7). This reconstruction is as an element of L2 ;we cannot recover ψ as an element of L2 from its Fourier transform. Thus (VII.10) holds “almost
everywhere” but not for all x.
The correct domain of the momentum operator p̂i is then as follows:D(p̂i ) = {ψ ∈ L2 (R3 ) | ki ψ̂ ∈ L2 (R3 )}.

(VII.11)On this domain one has p̂∗i = p̂i , i.e. the momentum operator is self-adjoint.
In general, any quantum-mechanical observable a should be a self-adjoint operator on some
Hilbert space. Bu Corollary VII.13, this implies that the spectrum of a is real, and we will see
later that the spectral theorem for self-adjoint operators enables one to construct yes-no questions
similar to χ∆ for the position operator (see (V.3) and surrounding text).

VII.4

The Hamiltonian

In case that the observable is the Hamiltonian, more detailed statements can be made, as follows.
Hamilton’s equations of motion for classical mechanics were given in (V.1) and (V.2). Quantum
mechanics has a single equation of motion, known as the Schrödinger equation. The role of theclassical Hamiltonian h = h(p, q) (i.e. a function on phase space) is now played by a certain operator
h on a Hilbert space H, whose specification is part of the definition of a given physical system.
The precise form of this operator is usually guessed from the form of the classical Hamiltonian.
For example, if H = L2 (R3 ) and the classical Hamiltonian is (V.2), Schrödinger took the quantum
Hamiltonian to be
3~2 X ∂ 2+ V.
(VII.12)h=−2m i=1 ∂x2i

Here m is the mass of a particle moving in a potential V , seen as a a multiplication operator. For
example, if the particle is an electron in a hydrogen atom, the potential is given by V (x) = −e/|x|.
It is clear that h cannot be defined on all of L2 (R3 ): the partial derivatives of an arbitraryψ ∈ L2 (R3 ) will not be defined (and even if they are defined the result may not be squareintegrable), and the product V ψ may not lie in L2 (R3 ) either, especially when the potential issingular (as is the case for the hydrogen atom and many other examples).
Hence h is an unbounded operator, and we have to find a domain on which it is self-adjoint.
Even for V = 0 this is a nontrivial problem. The solution is similar to the case of the momentum
operator: it turns out that the free Hamiltonian3h0 = −~2 X ∂ 22m i=1 ∂x2i

(VII.13)

47

VII.5. STONE’S THEOREM

is self-adjoint on the domain
D(h0 ) = {ψ ∈ L2 (R3 ) | k 2 ψ̂ ∈ L2 (R3 )},(VII.14)with k 2 = k12 + k22 + k32 . For nonzero potential the problem is very difficult, although most realistic
cases have been completely understood now [5, 8, 19, 20, 21, 24, 27]. All known examples support
the following interpretation of the spectrum of the Hamiltonian:• The discrete spectrum σd (h) corresponds to the bound states of the system;• The continuous spectrum σc (h) corresponds to the scattering states.
To clarify this, first note that if E ∈ σd (h), by definition there is an eigenvector ψE such thathψE = EψE . Hence ψE is a solution to the time-independent Schrödinger equation with energy
E. For example, for the hydrogen atom one has En = −me e4 /2~2 n2 , where me is the mass of theelectron and e is its charge, with the well-known eigenfunctions ψ(n,l,m) . Such an eigenfunction
describes a bound state, in which the electron forms a standing wave around the nucleus.
There is an interesting difference between the classical and the quantum-mechanical descriptionof bound states: a planet revolving around the sun is in a bound state, but it clearly displays timedependent behaviour (it moves around)! An electron in a bound state, on the other hand, evolves
according to the time-dependent Schrödinger equation. We will turn to the precise mathematicalmeaning of this equation in the next section, but for the moment we just write it down:hψ(t) = i~

dψ(t)
.
dt

(VII.15)This equation should be seen as the quantum-mechanical analogue of Hamilton’s equations (V.1).
If E ∈ σd (h), a possible solution of (VII.15) isψE (t) = e−itE/~ ψE .

(VII.16)

Although formally ψE (t) changes in time, physically nothing really happens, as ψE (s) and ψE (t) fors 6= t merely differ by a phase factor. Hence any expectation value (ψE (t), aψE (t)) is independentof t. So bound states in quantum mechanics are static, whereas in classical mechanics they are
dynamical. This makes the transition from quantum mechanics to classical mechanics in the limit~ → 0 very difficult.
In any case, given h = h∗ we may split up the Hilbert space H on which h acts by H =Hd ⊕ Hc , where Hd contains all eigenvectors of h ans Hc = Hd⊥ . States in Hc really move in time,and physically turn out to describe situations in which a particle moves in space in a nonclosed
trajectory. For example, think of a comet moving into the solar system with such a speed that it
is not captured by the gravitational pull of the sun, but moves out again. In atomic physics, thinkof an electron shot into an atom and, after being scattered, moving out again.

VII.5

Stone’s theorem

The time-dependent Schrödinger equation (VII.15) is very delicate, mathematically speaking. Let
us start with the left-hand side. The Hamiltonian h is generally an unbounded operator, so weshould worry about its domain and possible self-adjointness (in order to qualify as an observable).
The right-hand side involves a limit, since by definitiondψ(t)ψ(t + s) − ψ(t):= lim.
s→0dts(VII.17)Since for fixed t the object ψ(t) is an element of a Hilbert space H, the natural meaning of thislimit is to take it in the natural norm of H (i.e., as always, the one derived from the inner product).
The existence of the limit then means: for each fixed t there is a vector ψ̇(t) ∈ H such that
lim

s→0

ψ(t + s) − ψ(t)− ψ̇(t) = 0.
s(VII.18)

48

CHAPTER VII. SPECTRAL THEORY FOR SELFADJOINT OPERATORS

If it exists, the vector ψ̇(t) is by definition the time-derivative dψ(t)/dt (as the notation alreadysuggests). But why should this limit exist?
Hence both the left-hand side and the right-hand side of the time-dependent Schrödinger equation (VII.15) are problematic. Stone’s Theorem provides a complete mathematical meaning of
the time-dependent Schrödinger equation and in fact relates the problem on one side to that on
the other. In doing so, it provides an explanation of self-adjointness as well. Stone’s Theorem
is generally regarded as a highlight in the interaction between Hilbert space theory and quantum
mechanics (although it may be considered a theorem in pure mathematics if so desired).
Stone’s theorem can be read in two directions:
1. Given the Hamiltonian h as a self-adjoint operator, it defines the time-evolution ψ(t).
2. Given the time-evolution ψ(t), it defines the Hamiltonian h as a self-adjoint operator.
Thus the theorem provides a relationship between the global time-evolution and the ‘infinitesimal’
time-evolution given by the Hamiltonian and the Schrödinger equation.
We already know what a self-adjoint operator is. To state the theorem, we need to define thenotion of time-evolution on a Hilbert space. We motivate this definition by some intuition fromquantum mechanics. Physicists solve the time-dependent Schrödinger equation with initial value
ψ(0) = ψ by
ψ(t) = u(t)ψ,(VII.19)
whereu(t) = e−ith/~ .

(VII.20)

Here we assume that the Hamiltonian h is time-independent. Indeed, if h is bounded (a situationthat rarely occurs in practice except when H is finite-dimensional) the exponential in (VII.20)
can simply be defined by a norm-convergent power-series (exercise!). When h is not bounded theexponential cannot be defined in this way. We will define the exponential of an unbounded operator
in the next chapter using spectral calculus.
Some desirable properties of u(t) may be read off nonetheless from “Australian style” formal
computation with the motto no worries about any mathematical problem that might arise. We
then take these heuristic properties as axioms.
First, we expect that (ψ(s))(t) = ψ(s + t), and this indeed follows from (VII.20) and the formal
computation exp(ta) exp(sa) = exp((s + t)a), where a is any operator. Indeed, this is rigorously
true when a is bounded. This translates into the propertyu(s)u(t) = u(s + t)

(VII.21)

for all s, t ∈ R. Furthermore, one clearly hasu(0) = 0(VII.22)

lim u(t)ψ = ψ(VII.23)and the strong continuity property
t→0

for each ψ ∈ H. for each s ∈ R. Finally, each u(t) is unitary.3 This follows from (VII.20) by aformal computation:u(t)u(t)∗ = e−ith/~ (e−ith/~ )∗ = e−ith/~ eith /~ = e−ith/~ eith/~ = 1,∗

and similarly for u(t)∗ u(t) = 1. Here we have used h∗ = h.
To some up, intuition based on the Schrödinger equation suggests the following idea.4
3 Recall that an operator u is unitary when uu∗ = u∗ u = 1.
4 Those familiar with groups and representations will recognize that this is just the definition of a continuous
unitary representation of R as a topological group. Condition (VII.23) can be shown to be equivalent to thecontinuity of the map ht, ψi 7→ u(t)ψ from R × H to H.

49

VII.5. STONE’S THEOREM

Definition VII.16 A time-evolution on H is a map R → B(H), t 7→ u(t), with the properties(VII.21), (VII.22) and (VII.23) and where u(t) is unitary for each t.
We now state Stone’s theorem. We put ~ = 1 for simplicity (put it back as an exercise!).
Theorem VII.17
domain1. Let t 7→ u(t) be a time-evolution on H. Define an operator h by theu(s) − 1ψ exists}s→0s

D(h) : {ψ ∈ H | lim(VII.24)and the actionu(s) − 1ψ.
s→0sThen D(h) is dense in H and h is self-adjoint.
hψ := i lim(VII.25)

2. Provided ψ ∈ D(h), for each t ∈ R the vector ψ(t) = u(t)ψ lies in D(h) and satisfies thetime-dependent Schrödinger equationhψ(t) = i

dψ(t)
.
dt

(VII.26)

3. Given a (densely defined) self-adjoint operator h on H there exists a unique time-evolution
on H that is related to h in the way just specified. Explicitly, one hasu(t) = e−ith ,(VII.27)

where the exponential function is defined by the spectral calculus.
The last sentence will be explained and proved in the next chapter.
We now prove part 1. Part 2 is an exercise in proving the interchange of limitsu(t + s)ψ − u(t)ψ
ψ(s) − ψ= u(t) lim
.
s→0
s→0
s
s
lim

(VII.28)

Part 3 will be proved in the next chapter. The proof of part 1 is based on the following lemma.
Lemma VII.18 Suppose h is a symmetric operator, i.e. D(h) is dense and(hϕ, ψ) = (ϕ, hψ)(VII.29)

for all ϕ, ψ ∈ D(h). If R(h + i) = H and R(h − i) = H, then h is self-adjoint.
This is contained in Theorem VII.12. In that theorem, as in all preceding material, it is assumed
that D(h) is dense in H. We prove this as follows (leaving the details as an exercise).
For any ψ ∈ H and n ∈ N defineZ ∞ψn := nds e−ns u(s)ψ.
(VII.30)
0Then ψn ∈ D(h) and ψn → ψ. Hence D(h) is dense in H.
The property (VII.29) is an easy application of the definition of h in the statement of Theorem
VII.17 and of Definition VII.16 (exercise).
Finally, one shows that R(h − i) = H using a trick: compute that (h − i)ψ1 = −iψ, with ψ1
defined by (VII.30) with n = 1. The property R(h − i) = H is proved in a similar way.
We have now checked the conditions of Lemma VII.18 and we are finished!
So far, we have thought of t 7→ u(t)ψ as the time-evolution of ψ. But nothing has relied on thisinterpretation (this is the power of abstraction in mathematics!). Consider the following example.
Take H = L2 (R) and define the map t 7→ u(t) by(u(t)ψ)(x) := ψ(x − t).

(VII.31)

50

CHAPTER VII. SPECTRAL THEORY FOR SELFADJOINT OPERATORSIt is an exercise to show that the map t 7→ u(t)ψ satisfies Definition VII.16. Using superior
foresight, we now rename the operator h defined in Part 1 of Theorem VII.17 as p̂. Then (VII.24)
is justD(p̂) := {ψ ∈ L2 (R) | ψ ′ ∈ L2 (R)},(VII.32)
where the derivative ψ ′ is defined as follows:ψ ′ (x) := lim

s→0ψ(x + s) − ψ(x)s(VII.33)

the limit not being meant pointwise in x but in the norm of L2 (R). Reinserting ~, the action(VII.25) of p̂ isdψp̂ψ = −i~ .
(VII.34)
dx
This is indeed the usual momentum operator, and Stone’s theorem states that it is self-adjoint on
the domain (VII.32). The theory of the Fourier transform finally shows that the domains (VII.32)
and (VII.11) coincide.
More generally, the domains of many operators of interest in quantum theory can be determinedthrough Stone’s theorem.

Chapter VIII

The spectral theorem
In this chapter we prove the spectral theorem for self-adjoint operators a. This theorem generalizes
the expansion or spectral decomposition (see Theorem IV.7)
X
λi pi(VIII.1)a=iof a self-adjoint compact operator (such as a hermitian matrix) in terms of its eigenvalues λi andthe projections pi of the corresponding eigenspaces (i.e., if ψ ∈ pi H then aψ = λi ψ). Let us notethe propertiespi ⊥ pi (i 6= j)(VIII.2)(since the eigenvectors belonging to different eigenvalues are orthogonal) andX
pi = 1(VIII.3)i(for the eigenvectors form an orthonormal basis).
This spectral decomposition plays a leading role in the probability interpretation of quantum
mechanics, which reads as follows in case the observable a = a∗ is a compact operator:
1. The possible values of the observable a : H → H that may be found when it is measured areits eigenvalues λi ∈ R;
2. The probability that λi occurs when a is measured when the system is in a state ψ ∈ H (i.e.
a unit vector) is
P (a = λi |ψ) = (ψ, pi ψ) = kpi ψk2 .
(VIII.4)
Here we have used the standard notation for conditional probabilities. In particular, when pi is
one-dimensional and ei ∈ pi H is a unit vector one has the Born rule
P (a = λi |ψ) = |(ei , ψ)|2 .

(VIII.5)

As we have pointed out before, in general a self-adjoint operator need not have any eigenvalues
at all (recall the example of the multiplication operator a(x) = exp(−x2 ) on H = L2 (R)), or
may have mixed spectrum consisting of both eigenvalues and continuous spectrum (for example,
a multiplication operator a ∈ C(Ω) on L2 (Ω) with compact support has eigenvalue 0 with infinite
multiplicity, since any ψ with support disjoint from a satisfies aψ = 0, whereas the remainder ofthe range of a will form its continuous spectrum).
Hence, given a = a∗ , we need to generalize:• The map λi 7→ pi ≡ p({λi }) from the spectrum of a to the set of projections on H;• The expansion (VIII.1).
51

52

CHAPTER VIII. THE SPECTRAL THEOREM

VIII.1

Spectral measures

We start with the former. The appropriate generalization turns out to be a map B 7→ p(B) fromthe Borel subsets1 of R to the set of projections on H. This map, called the spectral measure
on R defined by a, will have the following properties.
Definition VIII.1 A map B 7→ p(B) from the Borel subsets of R to the set of projections on H
is called a spectral measure when:
1. p(∅) = 0;
2. p(R) = 1;
3. p(A ∩ B) = p(A)p(B);
4. p(A ∪ B) = p(A) + p(B) when A ∩ B = ∅;5. p(∪n Bn ) = ∨p(Bn ), where ∨pn is the smallest projection p such that pn ≤ p for all n.
Note that 1. follows from 4. It follows from the third item that all p(B) commute among eachother when B varies, since A ∩ B = B ∩ A. It also follows thatp(A)p(B) = 0 if A ∩ B = ∅.

(VIII.6)

It will also turn out that p(B) = 0 if B ∩ σ(a) = ∅ (see Cor. VIII.10), so that the spectral measureof a may be regarded as a map from the Borel sets in σ(a) to the set of projections on H. This
property allows us to write down the map B 7→ p(B) for compact self-adjoint operators: if a iscompact and a∗ = a one has
X
pi .
(VIII.7)
p(B) =i|λi ∈B

In particular, pi = p({λi }) as already mentioned. The property (VIII.2) is then a special case of(VIII.6), and (VIII.3) follows from properties 2, 4 and 5 in Definition VIII.1.
Given a self-adjoint operator a, we will construct a spectral measure B 7→ p(B) by means ofp(B) = χB (a).

(VIII.8)

Here χB : R → {0, 1} is the characteristic function of B, but we will show how to define it with aself-adjoint operator instead of a real number as its argument. In terms of this spectral measureassociated to a, the probability interpretation of quantum mechanics for arbitrary self-adjoint
operators is as follows:
1. The possible values of a : D(a) → H that may be found when it is measured are the elementsof its spectrum σ(a);2. The probability that some value within B ⊂ σ(a) occurs when a is measured when the system
is in a state ψ ∈ H is
P (a ∈ B|ψ) = (ψ, p(B)ψ) = kp(B)ψk2 .
(VIII.9)For example, take the position operator q̂ on L2 (R) with domainD(q̂) = {ψ ∈ L2 (R) | xψ ∈ L2 (R)}

(VIII.10)

and action q̂ψ(x) = xψ(x); see (VII.5) and (VII.6). This operator is self-adjoint; see Proposition
VI.8. It turns out thatp(B) = χB(VIII.11)
1 If you are unfamiliar with this concept, think of just any “reasonable’ subset of R. For example, all open subsets
of R as well as all countable intersections of open sets are Borel, as are all closed subsets of R and all countable
unions thereof. In fact, it is practically (though not logically) impossible to construct a subset of R that is not Borel!

53

VIII.1. SPECTRAL MEASURES
as a multiplication operator on L2 (R). Hence (VIII.9) becomesZZdx |ψ(x))|2 .
dx χB (x)|ψ(x)|2 =P (q̂ ∈ B|ψ) =(VIII.12)

B

R

See also (V.3). More generally, the multiplication operator a on L2 (Ω) defined by â ∈ C(Ω) (i.e.
aψ = âψ) gives rise to the spectral measurep(B) = χâ−1 (B) ,(VIII.13)

where â−1 (B) = {x ∈ Ω | â(x) ∈ B}. Hence
P (a ∈ B|ψ) =Zâ−1 (B)dn x |ψ(x)|2 .

(VIII.14)

A completely different example is provided by the unit operator a = 1 = id, which leads top(B) =
p(B) =

1 if 1 ∈ B;
0 if 1 ∈/ B.

(VIII.15)(VIII.16)

Hence
P (id ∈ B|ψ) =P (id ∈ B|ψ) =1 if 1 ∈ B;
0 if 1 ∈/ B.

(VIII.17)(VIII.18)

In other words, the unit operator assumes the value 1 with probability one.
More generally, suppose σ(a) is discrete. In that case the spectral theorem below will take thesame form (VIII.1) as for compact operators. One then recovers (VIII.7), so thatX(ψ, pi ψ).
(VIII.19)
P (a ∈ B|ψ) =i|λi ∈B

Let us writePψ (B) := (ψ, p(B)ψ),(VIII.20)

for the probabilities defined by a given spectral measure on H and a unit vector ψ ∈ H; if thespectral measure is derived from a self-adjoint operator a one of course has Pψ (B) = P (a ∈ B|ψ)as in (VIII.9). It now follows from Definition VIII.1 and kψk = 1 that the numbers Pψ (B) define
a probability measure on R in the following sense.
Definition VIII.2 A map B 7→ P (B) from the Borel subsets of R to [0, 1] is called a probability
measure when:
1. P (R) = 1;
2. P (∪n Bn ) =P

n P (Bn ) whenever Bi ∩ Bj = ∅ for i 6= j.

Note that P (A ∪ B) = P (A) + P (B) when A ∩ B = ∅ is a special case of the second property, fromwhich P (∅) = 0 follows by taking A = ∅.
This shows that a quantum-mechanical observable (i.e. a self-adjoint operator) a and a state(i.e. a unit vector) ψ in a Hilbert space H together define a probability measure on R through(VIII.8) and (VIII.20). This should be compared with the “classical” situation, in which one starts
with a measure space X, a probability measure2 µ on X, and a measurable function f : X → R.
From these data, one obtains a probability measure P on R by P (B) := µ(f −1 (B)). Clearly, X, µand f play the roles of H, ψ and a, respectively. Here R may be replaced by f (X) ⊂ R, just as R
may be replaced by σ(a) ∈ R in the Hilbert space situation.
2 This is a map from the measurable subsets Σ ⊂ X to [0, 1] satisfying µ(X) = 1 and µ(∪ Σ ) =n n
wheneverΣi ∩ Σj = ∅ for i 6=Rj. Just think of X = Rn and a continuous function ρ : Rn → R+Rnnd x ρ(x) = 1. Then µ(Σ) = Σ d x ρ(x).
Rnn µ(Σn )such thatP

54

CHAPTER VIII. THE SPECTRAL THEOREM

VIII.2

Construction of spectral measures

Now that we know the goal, we are going to make sense of (VIII.8). We do this for bounded
self-adjoint operators
Pna; the unbounded case will be dealt with later. The idea is this.
Pn For apolynomial p(x) = k=1 ck xk on R, it is clear what p(a) should mean, namely p(a) = k=1 ck ak .
We now approximate χB by polynomials (p)n pointwise, pn (x) → χB (x) for all x ∈ R (see below
for a caveat and a precise version). We then define χB (a) and hence p(B) by limn pn (a), where
the limit has to be taken in a certain way (see below). This procedure is justified by the following
lemma’s.
Lemma VIII.3 Let K = [−k, k] ⊂ R, k ∈ R+ . For each positive bounded (Borel) function
f : K → R there exists a bounded monotone increasing sequence (pn ) of polynomials on K thatconverges pointwise to f .
This means that0 ≤ p0 (x) ≤ p1 (x) ≤ · · · pn (x) ≤ pn+1 (x) ≤ · · · ≤ cfor some c > 0 and all x ∈ K, andlim pn (x) = f (x)n→∞(VIII.21)

for all x ∈ K. We will not prove this lemma from real analysis, as it has nothing to do with Hilbert
space theory.
Lemma VIII.4

1. If p is a real polynomial on K and p(x) ≥ 0 for all x ∈ K, then p(a) ≥ 0.

2. Consequently, if (pn ) is a monotone increasing sequence (pn ) of real polynomials on K
bounded by c, then the sequence of operators (an := pn (a)) satisfies
0 ≤ p0 (a) ≤ p(a)1 ≤ · · · pn (a) ≤ pn+1 (a) ≤ · · · ≤ c1.

(VIII.22)

Recall that a ≤ b for two bounded operators means that (ψ, aψ) ≤ (ψ, bψ) for all ψ ∈ H. The
second part clearly follows from the first part. The proof of this first part is based on anotherlemma, which is of independent interest:
Lemma VIII.5 If pq is the pointwise product of two (possibly complex-valued) polynomials p andq, then (pq)(a) = p(a)q(a), and similarly for p + q and cp, c ∈ R. Moreover, one has p(a)∗ = p(a)(where p is the complex conjugate of p).
The proof is a simple calculation (exercise). We now derive part 1 of Lemma VIII.4 from Lemma
VIII.5. Since p(x) ≥ 0 for all x ∈ K = [−k, k], we may write p asYYY
(VIII.23)
p(x) = c (x − ri ) (sj − x) ((x − tk )2 + ck ),ijkwith c ≥ 0, ck ≥ 0, ri < −k and sj > k. By Lemma VIII.4 we havep(a) = cYYY(a − ri ) (sj − a) ((a − tk )2 + ck ).
ij(VIII.24)

k

It follows from the definition of the operator norm that if a∗ = a and r < −kak, then a − r ≥ 0.
Similarly, if s > kak, then s − a ≥ 0.QThird, if t ∈ R then (a − t)2 ≥ 0. Finally, if operators bk allcommute and bk ≥ 0 for all k, then k bk ≥ 0. This shows that p(a) ≥ 0.


We say that a sequence (an ) of bounded self-adjoint operators is monotone increasing boundedwhen
a0 ≤ a1 ≤ · · · an ≤ an+1 ≤ · · · ≤ c1
(VIII.25)
for some constant c.

VIII.2. CONSTRUCTION OF SPECTRAL MEASURES

55

Lemma VIII.6 Each monotone increasing bounded sequence (an ) of bounded self-adjoint operators converges strongly to a bounded self-adjoint operator. This means that for any fixed ψ ∈ H
one haslim an ψ = aψ
(VIII.26)nfor some bounded self-adjoint operator a. We write this asa = s- lim an .
n

(VIII.27)

The proof relies on a generalized Cauchy–Schwartz inequality: if a ≥ 0, then|(ϕ, aψ)|2 ≤ (ψ, aψ)(ϕ, aϕ)(VIII.28)

for all ψ, ϕ ∈ H. The proof is an exercise. Using this inequality, one easily shows that (an ψ) is a
Cauchy sequence in H. We call its limit aψ, and this defines (VIII.26). Then a is bounded. For
we have kaψk ≤ k(a − an )ψk + kan ψk; the first term is as small as you like for large n, whereaskan ψk ≤ ckψk because an ≤ c1. Hence kaψk ≤ ckψk for all ǫ > 0. Self-adjointness is easy as well:aψ = limn an ψ and a∗n = an for all n imply(ϕ, aψ) = lim(ϕ, an ψ) = lim(an ϕ, ψ) = (aϕ, ψ).
nnNow take K such that [−kak, kak] ⊂ K (see below why) and use Lemma VIII.3 to find abounded monotone increasing sequence (pn ) of polynomials on K that converges pointwise to agiven positive bounded Borel funtion f . We then put
f (a) := s- lim pn (a), where pn (x) → f (x) ∀x ∈ K.

(VIII.29)

This is independent of the approximating sequence (pn ).
We are now in a position to make sense of (VIII.8) for a bounded and self-adjoint; just takef = χB in (VIII.29) to defineχB (a) := s- lim pn (a), where pn (x) → χB (x) ∀x ∈ K.

(VIII.30)

To show that the ensuing map B 7→ p(B) is indeed a spectral measure, we need a strengtheningof Lemma VIII.4. First, note that any bounded Borel function f : R → C can be decomposed as
f = f0 − f1 + i(f2 − f3 ),(VIII.31)

with all fi ≥ 0. Definition (VIII.29) then applies to each of the four terms in the decomposition of
f , and we can define
f (a) := f0 (a) − f1 (a) + i(f2 (a) − f3 (a)).
(VIII.32)
Theorem VIII.7 Let a∗ = a be a bounded operator on H. Let again K = [−k, k] ⊂ R be suchthat [−kak, kak] ⊂ K.
1. The map f 7→ f (a) from the space of bounded Borel functions f : K → C to the bounded
operators on H is an algebra homomorphism in the sense that(t1 f + t2 g)(a) =(f g)(a) =f (a) =

t1 f (a) + t2 g(a);f (a)g(a);(VIII.33)(VIII.34)

f (a)∗ .

(VIII.35)

Here f g is the pointwise product of f and g and ti ∈ R.
2. The map f 7→ f (a) preserves positivity in the sense thatf (x) ≤ g(x) ∀x ∈ K ⇒ f (a) ≤ g(a).

(VIII.36)

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CHAPTER VIII. THE SPECTRAL THEOREM

3. If (fn ) is a bounded monotone increasing sequence of (Borel) functions on K converging
pointwise to f , then f (a) = limn fn (a) (strong limit, i.e., f (a)ψ = limn fn (a)ψ for eachψ ∈ H).
4. One has
kf (a)k ≤ kf k∞ = sup |f (x)|.

(VIII.37)

x∈K

To prove this, we need a new description of the strong limit a in (VIII.27). Namely, under theconditions of Lemma VIII.6 one has
a := s- lim an = sup an ,n

(VIII.38)nthe least upper bound of the an under the partial ordering ≤ for operators. This means that a is
the unique positive operator for which an ≤ a and if an ≤ b ≤ a, then b = a. Namely, an ψ → aψ
implies (ψ, an ψ) → (ψ, aψ), hence by real analysis (ψ, aψ) = sup{(ψ, an ψ)}. If b is as above then(ψ, an ψ) ≤ (ψ, bψ) ≤ (ψ, aψ). Taking the supremum over n then gives (ψ, bψ) = (ψ, aψ) for all ψ.
Now (exercise), it can be shown that because b∗ = b and a∗ = a this equality implies b = a.
This leads to an easy proof of the theorem. One splits f and g into four positive parts asabove. Then (VIII.33) and (VIII.35) are easy. To prove (VIII.34), its is sufficient to show thatf 2 (a) = f (a)2 (why?). Using Lemma VIII.5 and realizing that if pn → f pointwise then p2n → f 2 ,this comes down to showing thats- lim(b2n ) = (s- lim bn )2 ,n

(VIII.39)nfor bn = pn (a). But this is true in general: if (bn ) is a monotone increasing bounded sequenceof bounded self-adjoint operators strongly converging to b, then, using (VIII.38) the property(VIII.39) is equivalent tosup{b2n } = b2 .
(VIII.40)
This is indeed correct: if c := sup{b2n } is the left-hand side, then by definition(ψ, cψ) = sup{(ψ, b2n ψ)} = sup{kbn ψk2 } = lim kbn ψk2 = k lim bn ψk2 = kbψk2 = (ψ, b2 ψ).
nnThis holds for all ψ ∈ H, so that c = b2 .
Eq. (VIII.36) follows from Lemma VIII.4. The third claim follows by approximating each fn
pointwise by polynomials; we leave the details to the reader as an exercise. The last claim will be
proved at the end of the next section.

Corollary VIII.8 The map B 7→ p(B) defined by (VIII.8) in terms of a bounded self-adjoint
operator a is a spectral measure (cf. Definition VIII.1).
Indeed, the properties 1-5 in Definition VIII.1 faithfully reflect the following properties op characteristic functions:1. χ∅ = 0;
2. χR = 1;
3. χA∩B = χA χB ;4. χA∪B = χA + χB when A ∩ B = ∅;5. χ∪n Bn = sup{χBn }.
Finally, the spectral measure associated to a is related to the spectrum σ(a) of a in the following
way.
Proposition VIII.9 One has λ ∈ σ(a) iff p(λ − ǫ, λ + ǫ) 6= 0 for all ǫ > 0.

57

VIII.3. THE SPECTRAL THEOREM FOR BOUNDED OPERATORS

Write this as A ⇔ B. To prove the “⇒” direction, we prove that notB ⇒ notA, i.e. that ifp(λ − ǫ, λ + ǫ) = 0 for some ǫ > 0, then λ ∈ ρ(a). Indeed, consider g : R → R defined byg(x) := (x − λ)−1 for x ∈/ (λ − ǫ, λ + ǫ) and g(x) := 0 for x ∈ (λ − ǫ, λ + ǫ). Then g is bounded,hence g(a) is defined. In what follows, we repeatedly use part 1 of Theorem VIII.7. First, sinceg(x)(x − λ)χ(λ−ǫ,λ+ǫ)c (x) = χ(λ−ǫ,λ+ǫ)c (x)for all x ∈ R, one hasg(a)(a − λ)χ(λ−ǫ,λ+ǫ)c (a) = χ(λ−ǫ,λ+ǫ)c (a).

(VIII.41)

However, sinceχ(λ−ǫ,λ+ǫ) + χ(λ−ǫ,λ+ǫ)c = 1
one hasχ(λ−ǫ,λ+ǫ) (a) + χ(λ−ǫ,λ+ǫ)c (a) = 1.

(VIII.42)

But χ(λ−ǫ,λ+ǫ) (a) = p(λ − ǫ, λ + ǫ) = 0 by assumption, so that (VIII.42) yields χ(λ−ǫ,λ+ǫ)c (a) = 1.
Hence (VIII.41) reads g(a)(a − λ) = 1. Similarly, (a − λ)g(a) = 1. It follows that g(a) is a bounded
inverse to a − λ, so that by definition λ ∈ ρ(a).
To prove B ⇒ A, we use Theorem VII.15. Assume p(λ − ǫ, λ + ǫ) 6= 0 for all ǫ > 0. Take
ǫ = 1/n and find a unit vector ψn ∈ p(λ − 1/n, λ + 1/n)H. Thenk(a − λ)ψn k ≤ k(a − λ)p(λ − 1/n, λ + 1/n)k ≤ 1/n;

(VIII.43)

the last estimate follows from (VIII.37), since
sup |(t − λ)χ(λ−ǫ,λ+ǫ) (t)| = ǫ.
t∈R

Hence k(a − λ)ψn k → 0, so that λ ∈ σ(a) by Theorem VII.15.



Corollary VIII.10 Let B 7→ p(B) be defined by (VIII.8). If B ∩ σ(a) = ∅, then p(B) = 0.
Now notice that for bounded a = a∗ one has σ(a) ⊆ [−kak, kak]. (Exercise: if z ∈/ [−kak, kak], thena − z is invertible, hence z ∈ ρ(a). This follows from Lemma VII.9). Hence everything we havedone so far is independent of the choice of K, as long as it is compact (so that every polynomial
p : K → R is bounded) and contains [−kak, kak].
Finally, it is an interesting exercise to derive the examples (VIII.11) etc. in the previous section
from the rigorous definition of the spectral measure in the present section.

VIII.3The spectral theorem for bounded operators

The map B 7→ P (B) defined by (VIII.8) generalizes the projections pi in the spectral theory ofcompact operators. We now turn to the generalization of the expansion (VIII.1). To state this, wefirst define a map t 7→ E(t) from R to the set of projections on H by
E(t) := p((−∞, t]) = s- lim p([s, t]),s→∞

(VIII.44)

where p(B) for B ⊂ R is given by (VIII.8). In other words,
E(t) = χ(−∞,t] (a).
Using part 1 of Theorem VIII.7, it is easily shown that E(t) is a projection, sinceE(t)2 = χ(−∞,t] (a)2 = χ2(−∞,t] (a) = χ(−∞,t] (a) = E(t)andE(t)∗ = χ(−∞,t] (a)∗ = χ(−∞,t] (a) = χ(−∞,t] (a) = E(t).

(VIII.45)

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CHAPTER VIII. THE SPECTRAL THEOREM

For example, combining (VIII.7) and (VIII.44), we have, for compact a = a∗ ,Xpi .
E(t) =(VIII.46)i|λi ≤tSimilarly, from (VIII.13) and (VIII.44) we obtain
E(t) = χa≤t := χ{x∈Rn |a(x)≤t} .

(VIII.47)

The map t 7→ E(t) defines a so-called spectral density in the sense that the following properties hold.
1. E is monotone increasing, that is, E(s) ≤ E(t) if s ≤ t. For projections, this is equivalent toE(s)H ⊆ E(t)H.
2. E is strongly right-continuous, that is, s- limε→0+ E(t + ε) = E(t).
3. s- limt→−∞ E(t) = 0 and s- limt→∞ E(t) = 1.
The first property follows from (VIII.36). The others follow from the third claim in Theorem(VIII.7) and the corresponding pointwise limits of χ(−∞,t] , e.g., limt→−∞ χ(−∞,t] (x) = 0 for allx ∈ R.
The spectral theorem now has the following form.
Theorem VIII.11 Let a be a bounded self-adjoint operator on H and let f : R → R be a bounded(Borel) function. ThenZf (a) =

dE(t) f (t).

(VIII.48)

In particular, the cases f (t) = t and f (t) = 1 generalize (VIII.1) and (VIII.3), respectively:Za =dE(t) t;
(VIII.49)
Z
1 =dE(t).
(VIII.50)

To make sense of this theorem, we need to define the integral. As we shall see, defining it
already proves the theorem. We proceed in four steps.
1. For f = χB we put

ZdE(t) χB (t) := p(B) = χB (a).

(VIII.51)

P
2. For a simple function f =k ck χBk (finite sum) with ck ≥ 0 we define the integral byextending the previous case by linearity, i.e.
Z
X
X
X
dE(t)
ck χBk :=ck p(Bk ) =ck χBk (a).
(VIII.52)kkkThis already proves the spectral theorem for simple functions, since if f is simple, then
(VIII.52) coincides with (VIII.49).

3. For general bounded positive (Borel) functions f , we use Lemma VIII.12 to find a sequence(sn ) of simple functions (subject to the conditions stated in the lemma) converging pointwise
to f to defineZZZ
dE(t) f (t) ≡ dE(t) lim sn (t) := s- lim dE(t) sn (t).
(VIII.53)nnBy (VIII.52), this equalsZdE(t) f (t) = s- lim sn (a).
n(VIII.54)

The existence of this limit follows in the same way as in (VIII.29), with polynomials replacedby simple functions.

VIII.3. THE SPECTRAL THEOREM FOR BOUNDED OPERATORS

59

4. Finally, the general (i.e. complex) case follows as in (VIII.31) and defining the integral bylinearity, i.e. if f = f0 − f1 + i(f2 − f3 ) with all fi ≥ 0, then
Z
Z
Z
Z
Z
dE(t) f (t) := dE(t) f0 (t) − dE(t) f1 (t) + i( dE(t) f2 (t) − dE(t) f3 (t)). (VIII.55)
If we now use the the third claim in Theorem (VIII.7), we see that the right-hand side of (VIII.54)
is just f (a). This proves Theorem VIII.11.

As we have seen, proving the spectral theorem is just a matter of defining the integral in theright way! The only substantial result on which the proof relies is this:Lemma VIII.12 Let K = [−k, k] ⊂ R, k ∈ R+ . For each positive bounded (Borel) function
f : K → R there exists a bounded monotone increasing sequence (sn ) of simple functions on K
that converges pointwise to f .
We still owe the reader a proof of part 4 of Theorem VIII.7. As easily follows from the definition,
one may estimate the norm of the integral (VIII.48) using the familiar rules, likeZZ
kf (a)k = k dE(t) f (t)k ≤ k dE(t)kkf k∞ = kf k∞ ,(VIII.56)
where we have used (VIII.50).
Finally, to recognize (VIII.1) as a special case of (VIII.49), we need the following result (cf.
Prop. VIII.9).3
Proposition VIII.13 One has λ ∈ σd (a) iff p({λ}) 6= 0, in which case p({λ}) equals the projection onto the eigenspace Hλ of a.
To prove this, we write p(λ) ≡ p({λ}) and denote the projection onto the eigenspace Hλ by pλ(provided λ ∈ σd (a)). We have p(λ) = χλ (a) by (VIII.8); we write χλ ≡ χ{λ} . Henceap(λ) = id(a)χλ (a) = (id · χλ )(a) = (λ1χλ )(a) = λ1(a)χλ )(a) = λp(λ).
Consequently, if p(λ) 6= 0 then pλ 6= 0, so that λ ∈ σd (a), andp(λ) ≤ pλ ,(VIII.57)

since p(λ)H ⊆ pλ H = Hλ , as we have just shown.
Conversely, let fn (t) := (inf{1, |λ − t|})1/n , so that fn → χR\{λ} pointwise. Hence by Theorem
VIII.7 (part 3) one has fn (a) → χR\{λ} (a) = p(R\{λ}). Now, if λ ∈ σd (a), so that pλ 6= 0, thenfn (a)pλ f = fn (λ)pλ f = 0(VIII.58)

for all f ∈ H; for in general one has f (a)pλ f = f (λ)pλ , as can be seen by approximating f bypolynomials (for which the property is clearly true) and using Theorem VIII.7. Letting n → ∞ in
(VIII.58) yields p(R\{λ})pλ = 0, or p(λ)pλ = pλ , as p(R\{λ}) + p(λ) = 1. In other words,p(λ) ≥ pλ .
With (VIII.57), we obtain p(λ) = pλ .

(VIII.59)
Consequently, if σ(a) = σd (a) the integral in (VIII.49) only picks up contributions from t =λi ∈ σd (a). Approximating the function f (t) = t in the appropriate way then yields (VIII.1)(exercise).

3 Recall that the discrete spectrum σ (a) consists of all eigenvalues of a.
d

60

VIII.4

CHAPTER VIII. THE SPECTRAL THEOREM

The spectral theorem for unbounded operators

The spectral Theorem VIII.11 is valid when a and f are bounded. If both are possibly unbounded,
the claim is as follows.
Theorem VIII.14 Let a : D(a) → H be a self-adjoint operator on and let f : R → R be a (Borel)function. ThenZf (a) =

dE(t) f (t)(VIII.60)

ZdPψ (t) |f (t)|2 < ∞}.

(VIII.61)

dE(t) t

(VIII.62)

Z(VIII.63)

is self-adjoint on

D(f (a)) = {ψ ∈ H |In particular,
a=
and

Z

D(a) = {ψ ∈ H |dPψ (t) t2 < ∞}.

R
Recall that the probability measure Pψ is given by (VIII.20). IntegralsR of the type dPψ (t) f (t)are defined as follows (cf. the proof of Theorem VIII.11): start with dPψ (t) χB (t) := Pψ (B),extend this to simple functions by linearity and for positive f defineZ
Z
Z
dPψ (t) f (t) = dPψ (t) lim sn (t) := lim dPψ (t) sn (t),(VIII.64)nnwhere sn are simple functions and limn sn = f pointwise. Finally, extend to arbitrary f using(VIII.31) and linearity.
We are going to prove Theorem VIII.14 using a method due to von Neumann [15], which derives
the spectral theorem for unbounded self-adjoint operators from the spectral theorem for unitary
operators. We omit the proof of (VIII.61).
Theorem VIII.15 Let u be a unitary operator on H. Then there exists a unique spectral density
E on H with the properties E(0) = 0 (hence E(t) = 0 for all t ≤ 0), E(2π) = 1 (hence E(t) = 1
for all t ≥ 2π), andZ 2πu=dE(t) eit .
(VIII.65)
0Furthermore, for bounded measurable functions f : T → C (where T is the unit circle in C) one
has
Z
2π

f (u) =

dE(t) f (eit ).

(VIII.66)

0The proof is practically the same as for self-adjoint operators, with the following difference. In
the self-adjoint case we started with polynomials p in a real variable t, defined p(a) in the obvious
way, and subsequently defined f (a) for arbitrary bounded (Borel) functions f by approximating
the latter by polynomials. The properties p(a)∗ = p(a) and p(a) ≤ q(a) if p(t) ≤ q(t) for all(relevant) t then implied the corresponding properties for arbitrary f (see Theorem VIII.7). These
properties hold by virtue of the equality a∗ = a. Now, for unitary operators u one instead hasu∗ u = uu∗ = 1.

(VIII.67)

Reflecting this change, we now start from polynomials in the variable
exp(it), so that the map
P
f 7→ f (u) isPinitially defined for functions of the form f (exp it) = k ck exp(itk), which is mapped
to f (u) := k ck uk . Here we may assume that 0 < t ≤ 2π. From this point onwards, the proof ofthe spectral theorem is the same as in the self-adjoint case.
VIII.4. THE SPECTRAL THEOREM FOR UNBOUNDED OPERATORS

61

To prove Theorem VIII.14 from Theorem VIII.15, von Neumann introduced an operator versionof the Cayley transform. For numbers, this is a map κ : R → C defined by
κ(t) :=t−i.
t+i(VIII.68)

This maps R homeomorphically (that is, continuously and bijectively with continuous inverse) toT\{1} (i.e., the unit circle in the complex plane minus the point 1). Moreover, since formallyκ(−∞) = κ(∞) = 1, it maps the one-point compactification Ṙ homeomorphically to T. The
inverse is1+z
κ−1 (z) = i,(VIII.69)
1−zwhere z ∈ T\{1}. Now let a be a self-adjoint operator. We see from Theorem VII.12 that −i ∈ ρ(a),so that a + i : D(a) → H is a bijection with bounded inverse (a + i)−1 : H → D(a). Composingwith a − i : D(a) → H, which is a bijection, too, we see thata−i
= (a − i)(a + i)−1a+i

κ(a) :=(VIII.70)

is a bijection from H to H. In a diagram, we have(a+i)−1

a−i

H −→ D(a) −→ H.
Furthermore, since a∗ = a one computes k(a + i)ψk = k(a − i)ψk for all ψ ∈ D(a), and puttingψ = (a + i)−1 ϕ we find kϕk = kκ(a)k. In other words, κ(a) is an isometry, and since an invertible
isometry is unitary, we conclude that κ(a) is unitary. We note thatR(1 − κ(a)) = D(a),(VIII.71)

since 1 − κ(a) = 2i(a + i)−1 , and as we have seen (a + i)−1 : H → D(a) is a bijection.
Conversely, let u be a unitary operator with R(1 − u)− = H. (For example, u = 1 does notsatisfy this assumption!) It is an easy exercise to show that this implies that 1 − u is injective, sothat 1 − u : H → R(1 − u) is invertible. We may then define an operator on the dense domain
D(κ−1 (u)) := R(1 − u) byκ−1 (u) := i

1+u
= i(1 + u)(1 − u)−1 .
1−u(VIII.72)

If u = κ(a) then clearly κ−1 (u) = a (including its domain), justifying the notation κ−1 . Consequently:
Proposition VIII.16 The operator Cayley transform a 7→ κ(a) establishes a bijection between
the class of self-adjoint operators a and the class of unitary operators u with R(1 − u)− = H.
Now let u = κ(a) and putin (VIII.66); this yields f (u) = κf (eit ) = κ−1 (exp(it))−1(u) = a, so thatZ 2π
1 + eit.
a=idE(t)1 − eit0

A chnage of variables t 7→ λ, where λ(t) := κ−1 (exp(it)) givesZa=dẼ(λ) λ,(VIII.73)(VIII.74)(VIII.75)

R

where Ẽ(λ) := E(t(λ)), where t(λ) is the unique t ∈ (0, 2π] for which κ(λ) = exp(it). With achange of notation, this is precisely (VIII.65), up to the domain. The argument for f (a) is similar,
starting from (VIII.66).
62

CHAPTER VIII. THE SPECTRAL THEOREM

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