Quantum Auctions
Tad Hogg
HP Labs
Palo Alto, CA
Pavithra Harsha
Cambridge, MA
Kay-Yut Chen
HP Labs
Palo Alto, CA
October 28, 2018
Abstract
We present a quantum auction protocol using superpositions to rep-
resent bids and distributed search to identify the winner(s). Measuring
the final quantum state gives the auction outcome while simultane-
ously destroying the superposition. Thus non-winning bids are never
revealed. Participants can use entanglement to arrange for correla-
tions among their bids, with the assurance that this entanglement is
not observable by others. The protocol is useful for information hiding
applications, such as partnership bidding with allocative externality or
concerns about revealing bidding preferences. The protocol applies to
a variety of auction types, e.g., first or second price, and to auctions
involving either a single item or arbitrary bundles of items (i.e., com-
binatorial auctions). We analyze the game-theoretical behavior of the
quantum protocol for the simple case of a sealed-bid quantum, and
show how a suitably designed adiabatic search reduces the possibilities
for bidders to game the auction. This design illustrates how incen-
tive rather that computational constraints affect quantum algorithm
choices.
http://arxiv.org/abs/0704.0800v1
1 Introduction
Quantum information processing [23] offers potential improvements in a va-
riety of applications. Computational advantages [26, 14] of quantum com-
puters with many qubits have received the most attention but are difficult to
implement physically. On the other hand, technology for manipulating and
communicating just a few qubits could be sufficient to create new economic
mechanisms by altering the information security and strategic incentives of
the underlying game.
Examples of quantum mechanisms include the prisoner’s dilemma [10,
11, 7, 8], coordination [17, 21] and public goods provisioning [3]. In partic-
ular, a quantum mechanism can significantly reduce the free-rider problem
without a third-party enforcer or repeated interactions, both in theory and
practice [2].
In this paper, we examine quantum mechanisms for another economic
scenario: resource allocation by auction [28]. While traditional auction
mechanisms can efficiently allocate resources in many cases, quantum auc-
tion protocols offer improvements in preserving privacy of the losing bids
and dealing with scenarios in which bidders care about what other bidders
win when multiple items are auctioned. Specifically, using quantum super-
positions to represent bids prevents the auctioneer and other bidders from
viewing the bids during the auction without disrupting the auction process.
Furthermore, the auction result reveals nothing but the winning bid and
allocation.
The first part of the paper introduces a general quantum auction protocol
for various pricing and allocation rules, multiple unit auctions, combinatorial
auctions and partnership bids. For simplicity, we focus on the sealed-bid
first-price auction. In this auction, each bidder has one opportunity to
submit a bid. The winner is the highest bidder, who pays the amount bid
for the item. This auction has been well studied both theoretically [28] and
experimentally [5, 4], and contrasts with iterative auctions in which bidders
can incrementally increase their bids depending on how others bid.
If the auction is not well-matched to the bidders preferences, it can intro-
duce perverse incentives and result in poor outcomes, such as lost revenue
for the seller or economically inefficient allocations where items are not al-
located to those who value them most. Thus it is important to examine
incentives introduced with a proposed auction design. In particular, our
auction protocol involves quantum search, which introduces incentive issues
beyond those examined in prior quantum games [11].
A full analysis of incentive issues is complicated, even for classical auc-
tions. In this paper we focus on two incentive issues arising from the quan-
tum auction protocol. The first incentive issue arises from the possibility
of manipulating the search outcome by altering amplitudes associated with
different bids. We show how to revise an adiabatic search method to correct
this incentive problem, thereby preserving the classical Nash equilibrium.
From a quantum algorithm perspective, this construction of the search il-
lustrates how incentive issues affect algorithm design, in contrast to the
more common concern with computational efficiency in quantum informa-
tion processing.
Second, the quantum search for the highest bid is probabilistic, i.e., does
not always return the highest bid. While the probability of finding the
correct answer can be made as high as one wishes by using more iterations
of the search, the small residue probability of awarding the item to someone
other than the highest bidder may change bidding behavior. As a step
toward addressing the effect of probabilistic outcomes, we show that, with
sufficient steps in the quantum search, altering choices from those of the
corresponding deterministic auction gives at most a small improvement for
that bidder.
The paper is organized as follows. Sec. 2 describes the quantum auction
and the bidding language encoding bids in quantum states. Sec. 3 describes
the quantum search method to find the maximum bid. After these sections
describing the auction protocol, in Sec. 4 we turn to strategic issues raised
by the quantum nature of the auction beyond those in the corresponding
classical auctions. Then, in Sec. 5 we give a game theory analysis of some
of these strategic possibilities and describe how simple modifications of the
quantum search improves the auction outcome, in theory. Sec. 6 generalizes
the results to auctions of multiple items, including combinatorial auctions.
Sec. 7 describes scenarios for which the quantum protocol offers likely eco-
nomic advantages in terms of information security and ability to compactly
express complex dependencies among items and bidders. Finally, Sec. 8 sum-
marizes the quantum auction protocol and highlights a number of remaining
economic questions.
2 Quantum Auction Protocol
In our auction protocol, each bidder selects an operator that produces the de-
sired bid from a prespecified initial state. The auctioneer repeatedly asks the
bidders to apply their individual operators in a distributed implementation
of a quantum search to find the winning bid. More specifically, the quantum
auction protocol for sealed-bid auctions involves the following steps:
1. Auctioneer announces conventional aspects of the auction: type of
auction (e.g., first or second price and any reservation prices), the
good(s) for sale, the allowed price granularity (e.g., if bids can specify
values to the penny, or only to the dollar), and the criterion used to
determine the winner(s), e.g., maximizing revenue for the seller
2. Auctioneer announces how quantum states will be interpreted, i.e., as
specifying a price if only one good is for sale, or a combination of price
and a set of goods if combinations are for sale; and also announces
the initial quantum state. This state uses p qubits for each bidder.
Auctioneer announces the quantum search procedure.
3. Each bidder selects an operator on p qubits. Bidders keep their choice
of operator private.
4. Auctioneer produces a set of particles implementing p qubits for each
bidder, initializing the set to the announced initial state.
5. Auctioneer and bidders perform a distributed search for the winner
Fig. 1 illustrates this procedure for two bidders and repeating the steps
of the search twice. Realistic search involves a larger number of steps. In
contrast with other quantum games, e.g., public goods, that involve just
one round of interaction, the search required to identify the winners involves
multiple rounds of interaction among the participants. The required number
of iterations depends on the search method. In practice, the auctioneer could
pick the number of iterations based on prior experience with similar auctions,
or from simulating several test cases using valuations randomly drawn from
a plausible distribution of values for the auction items. Alternatively, the
auctioneer could repeat the procedure several times (possibly with steps
from each repetition interleaved in a random order) and use the best result
from these repetitions.
auctioneer
auctioneer
auctioneer
bidder 1 bidder 2
bidder 1 bidder 2
start
measure state
announce result
Figure 1: Schematic diagram of distributed search procedure, showing re-
peated interactions between auctioneer and bidders, in this case two bidders
and two steps of the distributed search.
number of bidders n
number of items in auction m
number of qubits per bidder p
state of qubits for bidder j ψj
state of all qubits Ψ = ψ1 ⊗ . . .⊗ ψn
Table 1: Notation for the quantum auction.
This auction protocol uses a distributed search so bidders’ operator
choices remain private. Specifically, the search operation requiring input
from the bidders is applied locally by each bidder, giving the overall opera-
U = U1 ⊗ U2 ⊗ . . .⊗ Un (1)
where n is the number of bidders and Ui the operator of bidder i.
3 Quantum Auction Implementation
A quantum auction requires finding the winning bid and corresponding bid-
der. This procedure has two components: the interpretation of the qubits
as bids, and the search procedure to find the winner. The following two sub-
sections discuss these components in the context of a single-item auction.
Sec. 6 generalizes this discussion to multiple items.
3.1 Creation and interpretation of quantum bids
We define a bid as the amount a bidder indicates he is willing to pay for
the item. An allocation is a list of bids, one from each bidder. The quan-
tum auction protocol manipulates superpositions of allocations. We use an
allocation rule to indicate how allocations specify a winner and amount paid.
Example 1. Consider an auction of one item with three bidders, willing to
pay $1, $3 and $10 for the item, respectively. We represent these bids as
|$1〉, |$3〉 and |$10〉, and the corresponding allocation as the product of these
states, i.e., |$1, $3, $10〉 with the ordering in the allocation understood to
correspond to the bidders. A simple allocation rule selects the highest bidder
as the winner, who pays the high bid. In this example, this rule results in
the third bidder winning, and paying $10 for the item.
Each bidder gets p qubits and can only operate on those bits. Thus each
bidder has 2p possible bid values, and can create superpositions of these
values. A superposition of bids specifies set of distinct bids, with at most
one allowed to win. The amplitudes of the superposition affect the likelihood
of various outcomes for the auction. For a single-item auction, a bidder
will typically have only one bid. As discussed below, more complicated
superpositions are useful for information hiding. Specifically, bidder j selects
an operator Uj on p qubits to apply to the initial state for that bidder’s
qubits ψinit specified by the auctioneer. The resulting state, ψj = Ujψinit,
is a superposition of bids, each of the form
where b
i is bidder j’s
bid for the item. The subscript i indicates one of the possible bids that can
be specified with p qubits according to the announced interpretation of the
bits.
We define the subspace used by bidder j as the set of states spanned by
the basis eigenvectors in ψj . Only these basis vectors appear in allocations
relevant for the search. As bidders apply their operators during the search,
the superposition of allocations remains within the subspace of each bidder.
In this case, where each bidder applies an operator only to their own qubits,
the superposition of allocations is always a factored form, i.e., Ψ = ψ1 ⊗
. . . ⊗ ψn. More generally, groups of bidders could operate jointly on their
qubits, entangling their bids in the allocations as discussed in Sec. 7.
To exploit information hiding properties of superpositions, the state re-
vealed at the end of the search should specify only the bidder who wins the
item and the corresponding bid. To achieve this, instead of a direct repre-
sentation of bids, we interpret bids formed from the p qubits available to a
bidder as containing a special null value, ∅, indicating a bid for nothing.
This null bid has additional benefits in multiple item settings, as discussed
in Sec. 6 and Sec. 7.
Example 2. Consider bidder j with two qubits and the initial state ψinit =
|00〉 corresponding to the vector (1, 0, 0, 0), which is interpreted as the null
bid. The other bid states are |01〉, |10〉 and |11〉 corresponding to vectors
(0,1,0,0), (0,0,1,0) and (0,0,0,1). These three states are interpreted as three
bid values in some preannounced way, e.g., $1, $2 and $3, respectively.
The operator
1 0 1 0
0 1 0 1
1 0 −1 0
0 1 0 −1
gives the initial state ψj = Ujψinit as (|00〉+|10〉)/
2 and specifies the search
subspace whose basis is the first and third columns of Uj in this example.
Thus the possible allocations involve only |00〉 and |10〉 for this bidder, cor-
responding to the null bid and a bid of $2, respectively.
In the presence of a null bid, we consider an allocation to be a feasible
if it contains exactly one bid not equal to ∅. The corresponding allocation
rule assigns no winner to infeasible allocations and, for feasible allocations,
the winner is the single bidder in the allocation whose bid is not ∅, and he
pays the amount bid. This allocation rule corresponds to a first-price single-
item auction, except there can be no winner, analogous to the situation in
auctions with a reservation price when no bidder exceeds that price.
3.2 Distributed Search
The auctioneer must find the best state according to an announced crite-
rion, e.g., maximum revenue. Specifically, the auctioneer has a evaluation
function F assigning a quality value to each allocation. The function F
assigns a lower value to infeasible allocations than to any feasible one. An
example is F equal to the revenue produced by the allocation (if feasible)
and otherwise is −1.
The auctioneer uses quantum search to find the allocation in the subspace
selected by the bidders giving the maximum value for F (e.g., a feasible
allocation giving the most revenue to the auctioneer). This could be done via
repeated uses of a decision-problem quantum search [14, 1] as a subroutine
within a search for the minimum threshold value of F giving a solution to the
decision problem, e.g., with a classical binary search on threshold values or
using results of prior iterations of the decision problem [9]. Alternatively, we
could use a method giving the maximum value directly (e.g., adiabatic [12]
if run for a sufficiently long time or heuristic methods [15, 16] based on some
prior knowledge of the distribution of bidders values). For definiteness, we
focus on the adiabatic method.
The adiabatic search is conventionally described as searching for the
minimum cost state. We use this convention by defining a state’s cost to be
the negative of the evaluation function F . The adiabatic search procedure,
if run sufficiently slowly, changes the initial superposition into a final super-
position in such a way that the amplitude in each initial eigenstate maps
to the same amplitude in the corresponding final eigenstate, up to a phase
factor (for nondegenerate eigenstates). We refer to this mapping of initial
to final eigenstates as a perfect search. In practice, with a finite time for
the search, there will be some transfer of amplitude among the eigenstates
so the search will not be perfect in the sense defined here. Instead the auc-
tion outcome is probabilistic: the auction will not always produce the best
outcome when starting from the ground state. For example, an auction in-
tending to find the highest bid could sometimes produce the second highest
bid instead. Conventionally, the search operations are chosen so the uniform
superposition is the lowest cost initial eigenstate. In our case, bidders are
free to choose their operators and need not create uniform superpositions.
A discrete implementation of adiabatic search consists of the following
steps:
• The auctioneer selects a number of search steps S and parameter ∆.
These need not be announced to the bidders.
• The auctioneer initializes the state of all np qubits to Ψinit = ψinit ⊗
. . . ⊗ ψinit = |0, . . . , 0〉, with n factors of ψinit in the product, and
ψinit = |0〉 is the initial state for the p qubits for a single bidder.
• The auctioneer sends these initialized qubits to the bidders who use
their individual operators and then return the qubits to the auctioneer,
jointly creating the state
Ψ0 = UΨinit (3)
• For s = 1, . . . , S, the auctioneer and bidders update the state to
Ψs = UD(f)U
†P (f)Ψs−1 (4)
with f = s/S the fraction of steps completed. The bid operator U and
its adjoint U † are performed by sending bits to the bidders as described
in Sec. 2. The diagonal matrices D(f) and P (f) are described below.
• The auctioneer measures the state ΨS, resulting in specific values for
all the bits, from which the winner and prices are determined by the
allocation rule described in Sec. 3.1.
The diagonal matrix P (f) adjusts the phases of the amplitudes according
to the cost associated with each allocation. In particular, using the cost
c(x) = −F (x) for allocation |x〉, we have
Pxx(f) = exp (−ifc(x)∆) (5)
Similarly, the diagonal matrix D(f) adjusts amplitude phases as defined by
a function d(x):
Dxx(f) = exp (−i(1− f)d(x)∆) (6)
The key property of d(x) is assigning the smallest value, e.g., 0, to |0〉,
thereby making the first column of U the ground state eigenvector. Aside
from this key property, the choice of d(x) is somewhat arbitrary. The con-
ventional choice in the adiabatic method uses the Hamming weight of the
state, i.e., d(x) equal to the number of 1 bits in the binary representation of
x. However, as described in Sec. 5, other choices for d(x) can improve the
incentive properties of the auction.
The discrete-step implementation of the continuous adiabatic method [12]
involves the limits ∆ → 0 and S∆ → ∞, in which case the final state ψS
has high probability to be the lowest cost state. In practice, this outcome
can often be achieved with considerably fewer steps using a fixed value of
∆, corresponding to a discrete version of the adiabatic method [16].
4 Strategies with Quantum Operators
Ideally, an auction achieves the economic objective of its design (e.g. maxi-
mum revenue for the seller). In practice, an auction design may not provide
incentives for participants to behave so as to achieve this objective. Usu-
ally auction designs are examined under the assumption of self-interested
rational participants. In conventional auctions, strategic issues include mis-
representation of the true value, collusion among bidders and false name
bidding (where a single bidder submits bids under several aliases). Some
of these issues can be addressed with suitable auction rules, e.g., second
price auctions encourage truthful reporting of values. Developing suitable
designs of classical auctions in a wide range of economic contexts remains a
challenging problem [28].
Quantum auctions raise strategic issues beyond those of classical auc-
tions. In our case, every step of the adiabatic search requires each bidder
to perform an operation on their qubits. Ideally, the bidder should use the
same operator U for creating ψinit as in every step of the search in Eq. (4). In
addition, bidders should include the null bid in their subspaces. In the clas-
sical first-price sealed-bid auction, the bidder makes one choice: the amount
to bid. In our quantum setting, this choice amounts to selecting the sub-
space to use with the quantum search. The remaining freedom to select U ,
and possibly a different U for each step in the search, are additional choices
provided by the quantum auction.
Bidders may be tempted to exploit the flexibility of choosing operators in
several general ways. First, they could use a subspace not including the null
bid. Second, they could use a different operator for creating ψinit than they
use in the rest of the search, thereby producing an altered initial amplitude
that is not the ground state eigenvector. Third they could change operators
during the search. If any such changes give significant probability for low
bids to win, bidders would be tempted to make such changes and include
a low bid in their subspace, hoping to profit significantly by winning the
auction with a low bid.
The remainder of this section describes some strategic issues unique to
quantum auctions and possible solutions. We further discuss a game theory
analysis of some of these issues in Sec. 5.
4.1 Selecting the Subspace
The use of the null bid in our protocol raises the strategic issue illustrated
in the following example:
Example 3. Consider an auction of a single item with two bidders Alice
and Bob. Using operators producing uniform amplitudes for the sake of
illustration, they ought to apply operators that create
(|∅〉+ |bA〉) and 1√
(|∅〉+ |bB〉)
respectively, where bA and bB are their desired bids. The initial superposition
for all the qubits is the product of these individual superpositions, i.e., Ψ0 is
(|∅,∅〉+ |bA,∅〉+ |∅, bB〉+ |bA, bB〉)
If bidders use these same operators during the search, the search algorithm
finds the highest revenue allocation, i.e., giving the item to the highest bid-
der. Suppose instead Bob picks an operator with a one-dimensional subspace,
producing an initial state |bB〉 rather than including ∅. The product super-
position is then
(|∅, bB〉+ |bA, bB〉)
Since the search remains in this subspace and the second allocation is infea-
sible, the search will return |∅, bB〉 no matter what Alice bids. Thus Bob
always wins the item, and can win using the lowest possible bid.
This example shows bidders have an incentive to exclude the null set
from their subspace. If all bidders make this choice, there will be no feasible
allocations in the joint subspace and the auction will always give no winner.
For auctions with more than two bidders, selecting subspaces excluding ∅ is
a weak Nash Equilibrium for the quantum auction because any other choice
by a single bidder still results in no feasible allocations.
4.2 Altering Initial Amplitudes
Strategic choices for bidders also arise from the search procedure itself, even
when using the correct subspace consisting of ∅ and the desired bid. In
particular, the probabilistic outcome of the search means the optimal bid
according to the auction criterion (e.g., highest revenue) will not always win.
For the adiabatic search method, bidders could try to arrange for especially
tiny eigenvalue gaps between the state corresponding to the best outcome
and another state allowing them to win with a low bid. A sufficiently small
gap could make the number of steps the auctioneer selects insufficient to give
the optimal state with high probability and instead give a significant chance
of producing the more favorable outcome. However, because the eigenvalues
are a complicated function of the operators of all bidders, and individual
bidders do not know the choices made by others, it will be difficult for a
bidder to determine how to make such especially small gaps and do so in a
way that gives a favorable outcome. Nevertheless, even fairly small proba-
bilities for not finding the optimal state could alter the strategic behavior
of the bidders.
A more direct way a bidder can arrange for a low bid to win is by altering
the initial state of the adiabatic search to start not in the ground state but
in an eigenvector corresponding to one of the first few eigenvalues above
the ground state. The adiabatic search takes such eigenvectors, with high
probability, to an outcome in which a bid lower than the highest wins. While
a single bidder cannot create an arbitrary initial condition, one bidder can
ensure that it is not the ground state. For example, a bidder could chose an
operator that gives a nonuniform amplitude for the initial state, in particular
(|∅〉−|bA〉)/
2, while using the uniform state (|∅〉+|bA〉)/
2 as the ground
state through the remainder of the search in Eq. (4). This can result in
significant probability for a low bid to win, and so a bidder is tempted to
deviate from the nominal operator choice.
Fig. 2 illustrates this behavior. Instead of starting in the ground state,
0 0.2 0.4 0.6 0.8 1
high bid wins
low bid wins
infeasible
Figure 2: Correspondence between initial basis and the possible allocations
for a single item auction with two bidders in the standard adiabatic search.
During the search, as f increases from 0 to 1, the eigenvalues of the four
states change as shown schematically in the figure. The states for f = 0
correspond to both bidders starting with the ground state, |00〉, the two
states obtained if one of the bidders starts with a different superposition,
|01〉 and |10〉 (“single-bidder deviation states”), and the state of both bidders
starting with different superpositions, |11〉 (“2-bidder deviation state”).
the bidder’s choice gives the initial state as a linear combination of the
ground state and the single-deviation state for that bidder, denoted as |01〉
or |10〉 for the two bidders in Fig. 2. Here a “single deviation” state is
one that a single bidder can create, i.e., by operating on just the qubits
available to that bidder. The adiabatic search splits the degeneracy, thereby
giving some probability for the lowest bid to win and some probability for
an infeasible allocation.
More generally, bidder i uses this strategy by selecting two different
operators U initi and Ui to use for forming the initial state and during the
search, respectively. These choices result in different joint operators, in
Eq. (1), used in Eq. (3) and (4).
As with selecting a subspace without ∅, if many or all bidders make
this choice, the initial state will have significant amplitude in eigenvectors
corresponding to large eigenvalues, which produce infeasible outcomes and
0 0.2 0.4 0.6 0.8 1
high bid wins
low bid wins
infeasible
Figure 3: Correspondence between the initial basis and the possible alloca-
tions for a single item auction with two bidders in the search with permuted
initial eigenvalues.
hence a high probability for no winner. Thus with standard adiabatic search,
if everyone uses the same operator for both initialization and search, then
each bidder is tempted to use a different initialization operator and bid low,
gaining a chance to win with a low bid. However, if multiple bidders attempt
this, the outcome will most likely be an infeasible state, with no winner.
We can address this problem by reordering the eigenvalues given by the
d(x) function in Eq. (6) so that any change in initial operator by a single
bidder increases probability of infeasible allocation but not the probability of
any feasible allocation with a bid lower than the highest bid. This is possible
because bidders only have access to their own bits, so can only form initial
superpositions from a limited set of basis vectors. Fig. 3 illustrates the
resulting situation. We give an analysis of this approach in Sec. 5.2.
4.3 Changing Operator During Search
The distributed search of Eq. (4) has each bidder using the same operator
for every step of the search. Thus bidders may gain some advantage by
altering their operator during the steps of the search. Gradually changing
the operator during the search amounts to a different path from initial to
final Hamiltonian during the adiabatic search. Thus, provided the auction-
eer uses enough steps, such changes will have at most a minor effect on the
outcome probabilities unless the bidder can arrange for particularly small
eigenvalue gaps among favorable states. Such arrangement is difficult, par-
ticularly since the bidder does not know the choices of other bidders and
the auctioneer could treat the bits from the bidders in an arbitrary, unan-
nounced order.
More significant changes in outcome is possible with sudden, large changes
in the operator during search. Since the use of bidders operators gradually
decreases during the search (i.e., Dxx(f) given in Eq. (6) approaches the
identity operator as f approaches 1), the most problematic situation is for
an abrupt change in operator at the beginning of the search. After such
a change, the adiabatic search continues its gradual change of states, but
now instead of starting in the ground state, it will instead have a linear
combination of various states obtained by mapping the original basis onto
the basis after the change.
5 Quantum Auction Design
In this section, we focus on mechanism design to reduce incentive issues
arising from the quantum aspects of the auction. We analyze incentive is-
sues with the Nash equilibrium (NE) concept commonly used to evaluate
auctions [28]. A given set of behaviors for the bidders is an equilibrium
if no single bidder can gain an advantage (i.e., higher expected payoff) by
switching to another behavior. Specifically, Sec. 5.1 describes an approach
to encouraging bidders to include the null set in their bids. In Sec. 5.2 we
show that using the ground state eigenvector is a NE provided bidders do
not change the operators during the search. Sec. 5.3 then discusses how
the auctioneer can discourage bidders from changing operators. Sec. 5.4 de-
scribes how the auction can be made symmetric across the different bidders.
We focus on single-item auctions in this section, but the ideas extend to
quantum combinatorial auctions, as described in Sec. 6.
5.1 Checking for the Null Set
One approach to the incentive to exclude the null set, described in Sec. 4.1,
is for the auctioneer to perform a second search: for the allocation with the
most ∅ values. This search uses the same distributed protocol of Eq. (4)
but with separate qubits and a different cost function to define P (f), i.e.,
setting c(x) to the number of non-∅ values in the allocation x. Interleaving
the additional search in a random order within the steps of the search for
the winning bid prevents bidders from knowing which search a given step
belongs to. So bidders could not consistently select different operators for
the two searches.
If all bidders include ∅ in their selected subspace, this additional search
returns |∅,∅, . . .〉. Any bidder found not to have included ∅ could be ex-
cluded from winning the auction. At this point the auctioneer could either
announce there is no winner, or restart the auction for the remaining bid-
ders without announcing this restart. The adiabatic search has a small but
nonzero probability of returning the wrong result, which would then incor-
rectly conclude some bidder did not include ∅. As long as the probability of
such errors is smaller than the error probability of the search for the winner,
these errors should not greatly affect the incentive structure of the mech-
anism. Alternatively, the auctioneer could use a search completing with
probability one in a finite number of steps, i.e., with different choices of D
and P in Eq. (4), the auctioneer could implement Grover’s algorithm [14]
to search for the allocation |∅,∅, . . .〉 in the joint subspace of the bidders.
Since the auctioneer does not know the size of the subspaces selected by the
bidders, the auctioneer would need to try various numbers of steps [1] before
concluding |∅,∅, . . .〉 is not in the selected subspaces. Unlike the adiabatic
search, failure would only indicate some bidder had not included ∅, but not
which one. Thus the auctioneer’s only alternative in this case is to announce
the auction has no winner.
While this approach removes the immediate benefit of not including the
null bid, its affect on broader strategic issues in the full auction is an open
question.
5.2 The First-Price Sealed-Bid Auction
In this section we examine the incentive structure of the auction with per-
muted eigenstates described in Sec. 4.2. We first review how game the-
ory applies to auctions. We then consider the quantum auction when the
search runs long enough to give successful completion almost always (“per-
fect search”). Finally, we consider the more realistic case of search with
small, but not negligible, probability for non-optimal outcomes.
5.2.1 A Game Theory Approach to Auctions
Game theory is a common approach to evaluating auctions [20, 28]. Con-
sider n people bidding for an item, with person i having value vi for the
item. Unlike discrete choice games, such as the prisoner’s dilemma, a strat-
egy for a private value auction involves a bidding function b(v), mapping
a bidder’s value to a corresponding bid. Theoretical analysis of auctions
usually involves identifying a NE strategy, if any. This is a strategy for all
players such that no bidder gains by changing this strategy given everyone
else is using it. This focus on possible changes by a single bidder assumes
bidders do not collude.
A primary issue for auction behavior is how much participants know
about other bidders’ values. Such knowledge can affect the choice of bid.
The most popular model of such knowledge is independent private values,
where the vi are independently drawn from the same distribution. Each
bidder knows his own value, but not the values of other bidders. However,
the distribution from which values come is common knowledge, i.e., known
to all bidders, each bidder knows the others know this fact, and so on. A
final ingredient for the analysis is an assumption of bidders’ goals. For
illustration, we use the common assumption that bidders are risk neutral
expected utility maximizers, and within the context of the auction, utility
is proportional to profit.
We illustrate this approach for a first-price sealed-bid auction, in which
each bidder submits a single bid without seeing any of the other bids. This
corresponds to the auction scenario considered in this paper. The bidder
with the highest bid gets the item and pays the amount of his bid. Thus if
bidder i bids bi, his profit is vi−bi if he wins the auction and zero otherwise.
To avoid possibly losing money, bidders should ensure bi ≤ vi, and bids are
required to be nonnegative.
In the symmetric case where bidders’ values all come from the same
distribution, a NE is a bidding function b(v). A bidder’s expected payoff
is (v − b(v))P (b) where v is his value, b is his bidding function and P (b)
is the probability of winning if he is using b(v) (which is also the function
others use in equilibrium). Let F be the cumulative distribution of values,
i.e., probability a value is at most v, and n be the number of bidders. The
equilibrium condition leads to a differential equation satisfied by b(v) [28].
As a simple example, when v is uniformly distributed between 0 and 1,
F (v) = v and the NE is b(v) = (n − 1)v/n. Thus, in the equilibrium
strategy, a bidder bids somewhat less than his value and the bid gets closer
to the value when there is more competition, i.e., larger n.
If bidders have differing value distributions, a NE involves a set of bidding
functions, {bi(v)}. An auction may have multiple equilibria.
5.2.2 Behavior with Perfect Search
With perfect search and non-colluding bidders, if bidders use the same opera-
tors for every step of the search, including initialization, and pick a subspace
with the null bid then the adiabatic search described in Sec. 3.2 finds the
highest revenue state. We now show that the auctioneer can choose eigenval-
ues for the search so that bidders have no incentive to create an initial state
different from the ground state. This choice corresponds to the auctioneer
selecting an appropriate function d(x) in Eq. (6).
Suppose bidder i uses operator Ui, giving the overall operator U with
Eq. (1). Suppose all bidders except bidder 1 use the same operator to create
the initial state as they use for the subsequent search. But bidder 1 uses two
operators: U init1 to form the initial state and U1 for the search. Thus the
initial state produced by bidder 1, ψ1 = U
1 ψinit, i.e., the first column of
U init1 , is not necessarily equal to the first column of U1 that bidder 1 uses for
the subsequent search. Instead, ψ1 may have contributions from all columns
of U1, i.e.,
αi |i〉 (7)
where |i〉 corresponds to column i, ranging from 0 to 2p−1, of U1. Combining
with the initial state of all other bidders, Eq. (3) gives Ψ0 =
i αi |i, 0, . . . , 0〉,
instead of the initial ground state |0, 0, . . . , 0〉.
Significantly, because a bidder can only operate on the p qubits from the
auctioneer and not on any of the qubits sent to other bidders, a single bidder
can only create a limited set of “single-deviation” initial states. In the case
of bidder 1, these states all have the form |i, 0, . . . , 0〉. Similarly, if bidder
j is the one using different initial and search operators, the states all have
the form |. . . , 0, i, 0, . . .〉, where only the jth position can be nonzero. Thus,
among the 2np basis states in the full search space, aside from the correct
ground state, only n(2p−1) are possible states some single bidder can create
when all other bidders use the same operator for initialization and search.
More generally, k bidders can create superpositions of (2p − 1)k basis
states in which none of them use the ground state initially, by selecting
different operators for initialization and search. Thus there are
(2p − 1)k (8)
k-deviation states that some set of k bidders can create, while the other
n− k bidders use the ground state.
Our formulation has n(2p−1) feasible allocations, i.e., situations in which
exactly one of the bidders has a non-∅ bid while all other bidders have ∅.
To see this, each of the n bidders could have the non-∅ bid, and this bid
could have any of 2p − 1 values (since the remaining value for the bidder’s
bits represents ∅). The remaining n− 1 bidders have only one choice each,
i.e., ∅.
Suppose the auctioneer selects d(x) such that d(|0, . . . , 0〉) = 0 is the
lowest eigenvalue and d(x) for all single-deviation states x is the largest
value, with intermediate values for all other states. Provided the number
of infeasible allocations is at least equal to the number of single-deviation
states, a perfect search will then map every single-deviation state to an
infeasible allocation, resulting in no winner for the auction. This condition
amounts to
2np − n(2p − 1) ≥ n(2p − 1) (9)
The following claim shows that Eq. (9) always is true in an auction scenario.
Claim 1. Eq. (9) is true for all integers n, p ≥ 1
Proof. When p = 1, Eq. (9) reduces to 2n−1 ≥ n, which is true for all n ≥ 1.
We prove a stronger condition for p ≥ 2, namely there are enough in-
feasible states to handle up to n − 1 bidders deviating. Using Eq. (8), this
stronger condition is
2np − n(2p − 1) ≥
(2p − 1) = 2np − 1− (2p − 1)n (10)
with the k = 1 term in the sum corresponding to the right-hand side of
Eq. (9). Writing x ≡ 2p − 1, Eq. (10) becomes f(x, n) ≡ xn − nx+ 1 ≥ 0.
Since p ≥ 2, we have x ≥ 3. For this range of x and for n ≥ 1, f(x, n) is
monotonically increasing in both arguments. To see f is monotonic for x, the
derivative of f(x, n) with respect to x is n(xn−1 − 1) which is nonnegative
since n ≥ 1 and x > 1. Similarly, the derivative with respect to n is
x(xn−1 ln(x) − 1) which is at least 3(ln(3) − 1) > 0 since n ≥ 1 and x ≥ 3.
Thus for the relevant range of n and x, f(x, n) ≥ f(3, 1) = 1 so Eq. (10) is
true for all n ≥ 1 and p ≥ 2.
Combining these cases for p = 1 and p ≥ 2 establishes the claim.
Using this claim, we demonstrate the permuted eigenvalue choices re-
move incentives to alter the initial amplitudes:
Theorem 1. If (a) auctioneer chooses eigenvalues as described above, (b)
{b∗i (v)}ni=1 is an equilibrium for the first-price classical auction, and (c) bid-
ders include the null set as part of their bids and use the same operator in
each step in the search except, possibly, for the initial state, then the strategy
of using bidding functions {b∗i (v)}ni=1 and the same operator for their initial
state as they use in the search is a NE for corresponding quantum auction.
Proof. Without loss of generality, suppose only bidder 1 deviates and all the
other bidders use {b∗i (v)}ni=2 and the same operator for initialization and
search. Then, as described above, the initial state Ψ0 is
i αi |i, 0, . . . , 0〉
for some choice of amplitudes αi, with i ranging from 0 to 2
p − 1.
A perfect adiabatic search maps each of these states to a corresponding
allocation. In particular, with d(|0, . . . , 0〉) having the smallest value of
the function d(x), the lowest cost allocation is produced with probability
|α0|2. This allocation corresponds to the highest bid winning. Moreover,
each |i, 0, . . . , 0〉 with i 6= 0 has the largest value of d(x), and so, because
of Eq. (9), maps to an infeasible allocation, giving no winner and hence no
value to bidder 1.
Hence the expected value for bidder 1 is |α0|2V where V is the value of
the expected profit of the corresponding classical auction to bidder 1. Since
|α0|2V ≤ V , bidder 1 cannot gain from such a deviation.
Furthermore, there is no gain from deviating from the bidding function
b∗1(v) since it will only decrease V , because, by assumption, {b∗i (v)}ni=1 is a
NE for the corresponding classical auction.
Because of Eq. (9), this discussion applies to deviations by any single
bidder, not just bidder 1. Thus, using bidding function {b∗i (v)}ni=1 and using
the same operator for their initial state as they use in the search is a NE.
The stronger condition, Eq. (10), shows that the number of infeasible
states is enough to give no winner for any choice of initial amplitudes that
up to n − 1 bidders can produce, provided p ≥ 2. Thus if an auctioneer
implements a collusion-proof classical auction with the quantum protocol
and assigns infeasible states as described then the resulting quantum auction
is collusion-proof up to n− 1 bidders for initial amplitude deviations.
The choice for d(x) satisfying the above requirements is not unique.
As one example, let x be the state index in the full search space, running
from 0 to 2np − 1. Consider x as written as a series of n base-2p numbers,
|x1, x2, . . . , xn〉. Define
d(x) = −r(x) (mod n+ 1) (11)
where r(x) is number of nonzero values among x1, x2, ..., xn. The mod oper-
ation gives all d(x) values in the range 0 to n. For the initial ground state,
x = |0, . . . , 0〉, r(x) = 0 so d(x) = 0, and this is the smallest possible value.
Single-deviation states have exactly one of the xi nonzero, giving r(x) = 1
and d(x) = n, the largest possible value. More generally, all k-deviation
states have r(x) = k so d(x) = n + 1 − k. This function definition gives
values directly from the representation of the state x, so, in particular, the
auctioneer can implement it without any knowledge of the subspaces selected
by the bidders.
The assumption of perfect search is a sufficient but not necessary con-
dition for the proof of Theorem 1. The necessary conditions are more com-
plicated because we only need that every single bidder deviation maps to a
linear combination of infeasible states. Thus mixing among different single-
deviation states during search (e.g., due to small eigenvalue gaps among
those states), or among states corresponding to two or more bidders deviat-
ing, does not affect the proof.
5.2.3 Bounded Number of Search Steps
Theorem 1 shows the quantum auction has the same NE as the classical
first price auction if the search is perfect and each bidder uses the same
operator for every search step of Eq. (4). Since adiabatic search, run for a
finite number of steps, is not perfect we examine the effect on the NE of
an imperfect search. We show that the NE for perfect search, i.e., bidding
as in a classical first price auction and using the same operator initially
and during the search, is an ǫ-equilibrium for the auction with imperfect
search. Furthermore, ǫ converges to zero as the number of search steps goes
to infinity. A strategic profile is an ǫ-equilibrium [24] if for every player,
the gains of unilateral defecting to another strategy is at most ǫ. This
weaker equilibrium concept is useful in our case because determining how to
exploit imperfect search is computationally difficult. Specifically, with the
small eigenvalue gaps and degeneracy it is hard to know whether imperfect
search benefits a particular bidder. Thus computational cost will likely
outweigh the small possible gain. In this situation, an ǫ-equilibrium is a
useful generalization of NE.
We must prove that for any ǫ there exists an N so that if the search
process uses at least N steps, the equilibrium of the game with a perfect
search is also an ǫ-equilibrium when using the actual search. To do so,
we bound the possible gain from deviation based on prior knowledge of the
range of possible bidder values. That is, we assume the distribution of values
has a finite upper bound v̄. In our context, one such bound is the maximum
bid value expressible by the announced interpretation of each bidders qubits.
Theorem 2. If the conditions of Theorem 1 are met, and assuming the pos-
sible bidder values are bounded by v̄, for any ǫ > 0, there exists an N so that
the NE in the quantum auction with a perfect search, shown in Theorem 1,
is also an ǫ-equilibrium of the same auction with an imperfect search using
N search steps.
Proof. Let ph be the probability of the highest bid wins. Let pinf be the
probability of reaching an infeasible state. Then po = 1 − ph − pinf is the
probability of a bid other than the highest bid wins.
With the adiabatic search, with nonzero eigenvalue gaps, the probability
of correctly mapping the initial to final states converges to one as the number
of search steps increases. Thus for any δ > 0, there always exists a N where
po is at most δ.
We define an equilibrium expected payoff function for bidder i with value
v as π∗i (v), when all bidders use their equilibrium bidding functions.
Without loss of generality, from the perspective of bidder i with value
v, the probability of achieving the equilibrium payoff, π∗i (v), if that bidder
does not deviate is 1 − δ. Thus the expected payoff of deviating is at most
πdeviatei (v) ≤ (1− δ)π∗i (v) + δv̄ because (a) the most any bidder can gain is
bounded by v̄, and (b) with probability 1− δ the auction either produces no
profit (pinf) or is identical to a classical auction (ph).
The expected gain g from deviating is the expected payoff from deviating
minus the expected payoff with no deviation, i.e., g = πdeviatei (v) − π∗i (v) ≤
δ(v̄ − π∗i (v)), which in turn is at most δv̄. Thus for any choice of δ, there
always exists an N where the maximum deviation benefit is at most δv̄.
For any ǫ > 0, using δ = ǫ/v̄ in the above discussion shows there always
exists an N where the deviation is at most ǫ.
5.3 Testing for Changed Operators During Search
One approach to the incentive issue of changing operators during search,
described in Sec. 4.3, is for the auctioneer to test the bidders by randomly
inserting additional probe steps in the search.
Specifically, suppose at any step of the search the auctioneer, with some
probability, decides to check a bidder by sending a new set of qubits in a
known state |φ〉, while storing the qubits for the search until a subsequent
step. For the test step, the auctioneer sets D or P to the identity operator.
The state returned by the bidder is then U ′iU
i |φ〉 or U
i Ui |φ〉, depending on
which part of the search step in Eq. (4) the auctioneer is testing. Without
loss of generality, we consider the former case.
Ideally, the bidder uses the same operator, so U ′i = Ui and U
i is the
identity. Suppose the test state is formed from some operator V , randomly
selected by the auctioneer, |φ〉 = V |0〉. If U ′iU
i is not the identity, the re-
turned state has the form α |φ〉+β |φ⊥〉, where |φ⊥〉 is some state orthogonal
to |φ〉 and |α|2 + |β|2 = 1. The auctioneer then applies V †, giving
α |0〉+ β |a〉 (12)
for some value a 6= 0. The auctioneer then measures this state, getting
0 with probability |α|2, indicating the bidder passes the test. Otherwise,
the auctioneer observes a different value, indicating the bidder changed the
operator.
Hence the chance of getting caught depends on how often the auctioneer
checks, and how big a change the bidder makes in the operator. Larger
operator changes are more likely to be caught. This testing behavior is
appropriate as small changes are not likely to have much affect on the search
outcome, and instead simply act as an alternate adiabatic path from initial
to final states. This technique is especially useful for risk averse bidders
since then even a small chance to be caught might be enough to prevent
bidders from wanting to change operators.
5.4 Assigning Eigenvalues to Subspaces
Quantum search acts on the full space of superpositions of the available
qubits, i.e., in our case to all 2np configurations of items and bids. In the
auction context, bidders choose operators to restrict the search to a subspace
of possible bids, namely the ones they wish to make. Conceptually, the
search described above is then restricted to the subspace selected by the
bidders.
The search can also be viewed as taking place in the full space of 2np
configurations. The operator U appearing in the search algorithm is block
diagonal (up to a permutation of the basis states), with only the block
operating on the selected subspace relevant for the search outcome. This
view of the search is that of the auctioneer, who has no prior knowledge
of the subspace selected by each bidder. The operator U is not known
to any single individual: instead its implementation is distributed among
the bidders, with each bidder implementing a part of the overall operator.
The auctioneer chooses the eigenvalues for the initial Hamiltonian and the
ordering for the qubits assigned to each bidder. These choices, which could
change during the search, affect the incentive structure of the auction as
described in Sec. 5.2.
This section describes how the auctioneer’s choice of d(x) can give the
same eigenvalues when restricted to the subspace actually selected for the
search. For simplicity, we suppose each bidder uses a 2-dimensional sub-
space, consisting of ∅ and the desired bid for the single item. While not
essential for the NE results discussed above, uniformity with respect to sub-
space choices means bidders are treated uniformly, so convergence of the
search is independent of the order in which the auctioneer considers the
bidders.
5.4.1 An Example
Consider n = 2 bidders, each with p = 2 bits, representing 4 values: ∅
and three bid values 1, 2, 3. A set of 2-bit operators to form a uniform
superposition of the form (|∅〉+ |b〉)/
2 where b is the bid value, 1, 2 or 3,
is 1/
2 times
1 −1 0 0
1 1 0 0
0 0 1 −1
0 0 1 1
1 0 −1 0
0 1 0 −1
1 0 1 0
0 1 0 1
1 0 0 −1
0 1 1 0
0 −1 1 0
1 0 0 1
which we can denote as A1, A2, A3, respectively, with the first columns giving
the uniform superposition of the three possible bid values. If the bidders
select bids b1, b2, respectively, the overall operator for the search is U =
Ab1 ⊗ Ab2 , used in Eq. (4) to perform each step of the search. Thus in this
case there are 9 possible subspaces the two bidders can jointly select. Up to
a permutation, U is block diagonal with the block containing the nonzero
entries of the first column, and hence all the nonzero amplitude during the
search, equal to
1 −1 −1 1
1 1 −1 −1
1 −1 1 −1
1 1 1 1
The search using U in the full 4-bit space is thus equivalent to one taking
place in the 2-bit subspace selected by the two bidders using this operator
The auctioneers’ choice of eigenvalues, i.e., the function d(x) used in
Eq. (6) should ensure the uniform superposition within the subspace defined
by the two bidders has the lowest value, say 0, and all other eigenstates have
larger values.
One possibility is the standard choice for the diagonal values d(x) when
searching in the full space of 24 states defined by the np = 4 bits, namely
the Hamming weight of each state, i.e., the number of 1 bits in its binary
representation, ranging from 0 to 4.
An alternative approach is picking d(x) so eigenvalues for the four states
appearing in V have the same values as they would have with using the
Hamming weight for a 2-bit search, ranging from 0 to 2. Doing so requires
selecting the eigenvalues to match the corresponding Hamming weights for
any choices the bidders make among A1, A2, A3. In this example, each bidder
has 2 qubits, so can represent 4 states, which we denote as |0〉 , . . . , |3〉. The
states for both bidders are products of these individual states, |0, 0〉 , . . . , |3, 3〉.
Examining the 9 possible cases for U , shows a consistent set of choices is
d(|x, y〉) equal to the number of nonzero values among x, y. With this d(x),
the adiabatic search in the subspace selected by the bidders is identical to
the standard adiabatic search for two bits. This choice treats both bidders
identically.
In this case we see the auctioneer can arrange the adiabatic search to
operate symmetrically no matter what choice of subspace each bidder makes
(i.e., no matter what value each bidder decides to bid). Thus from the point
of view of the bidders, the search, in effect, takes place within the subspace
of possible values defined by their bid selections.
5.4.2 General Case
For arbitrary numbers of bidders n and bits p, we consider a single-item
auction so each bidder would, ideally, pick an operator giving just two terms,
with b(j) the bid of bidder j for the single item and no bits needed to specify
which item the bidder is interested in. The choice of b(j) corresponds to
the bidder picking a 2-dimensional subspace of the 2p possible states. The
product of these subspaces gives a subspace S of all np qubits used in the
auction. The subspace S has dimension 2n and its states xS can be viewed
as strings of n bits. More specifically, we suppose bidder j implements the
operator Uj such that the rows and columns corresponding to ∅ and b
have nonzero values only for positions ∅ and b(j). That is, the elements of
Uj for these two values form a 2× 2 unitary matrix.
If the auctioneer knew the subspace S, the eigenvalue function d(x) used
in Eq. (6) could be selected to match any desired function dS(xS) of the
states in xS ∈ S. Without such knowledge, this is possible only for some
choices for dS .
Theorem 3. Provided dS(xS) depends only on the Hamming weight of the
states xS, a single choice of d(x) in the full space corresponds to dS(xS) in
all possible subspaces the bidders could select that include the null set.
Proof. Consider the full operator U given by Eq. (1). For the element Ux,y,
express the np bits defining the states x and y as sequences of p-bit values,
x1, . . . , xn and y1, . . . , yn, respectively, with each xi and yi between 0 and
2p − 1. From Eq. (1),
Ux,y =
(Ui)xi,yi
The matrix U is of size 2np × 2np while each Ui is of size 2p × 2p.
Consider the first column of U , i.e., y = 0. Ux,0 is nonzero only for those
x such that all the (Ui)xi,0 are nonzero. For this to be the case, each xi is
either 0 (corresponding to |∅〉 for that bidder’s superposition) or xi = b(i),
i.e., the bid value. Similarly, for all columns with each yi equal to 0 or b
These values for x, y are precisely the states in the selected subspace of the
bidders, S. For these choices of xi, yi, we can map 0 (i.e., p bits all equal to
zero) to the single bit 0, and each b(i) (specified by values for p bits) to the
single bit 1. This establishes a one-to-one mapping from states in the full
space, of np bits corresponding to the product of bidders’ superpositions,
to states in the subspace treated as n-bit vectors. Thus a function dS(xS)
applied to the subspace that depends on the Hamming weight, i.e., the
number of 1 bits in xS, is the same as a function on the full space depending
on the number of nonzero xi values in x = x1, . . . , xn.
We must show that a single choice of function d(x) in the full space
maps to the desired dS(xS) in any choice of bidder subspaces. To see this
is the case, consider any state in the full space x = x1, . . . , xn. Among
these xi, suppose h are nonzero, denoted by xa1 , . . . , xah . This state x
will appear in all selected subspaces in which bidder aj bids b
(aj) = xaj ,
for j = 1, . . . , h, and the remaining bidders have any choice of bid. That
is, x appears in (2p − 1)n−h possible subspaces S. Since x has exactly h
nonzero values, in each of these possible subspaces it maps to a state xS
with exactly h bits equal to 1, i.e., it has the same Hamming weight, h,
in all possible subspaces in which it appears. Thus any choice of function
dS(xS) depending only on the Hamming weight of xS will have the same
value in all these possible subspaces. This observation allows the auctioneer
to select that common value as the value for d(x), consistently giving the
desired eigenvalue function for any possible subspace. Since this holds for
all values of h, the auctioneer can operate in the full space with identical
search behavior no matter what subspace the bidders select.
For the auctioneer to operate without knowledge of the actual subspace
selected by the bidders and treat bidders identically, we need d(x) to map
to the same function on any subspace selected. In this case, the search
proceeds exactly as if the auctioneer did know the subspace choices made
by the bidders. The theorem gives one type of function for in which this is
the case. In particular, Eq. (11) is an example of a function satisfying this
theorem.
6 Multiple Items and Combinatorial Auction
While the paper focuses on the single item first-price sealed-bid auction, the
quantum protocol can apply to multiple items by changing the interpretation
of the bids, i.e., the bidding language. Such changes affect the counting of
deviation and feasible states, so we must check the validity of Theorem 1.
In the single item case, each bidder uses the p qubits to specify the bid
amount. With multiple items, the bid must specify both the items of interest
and a bid amount for the items. Various bidding languages can encode this
information.
For multiple items, we divide the p qubits allocated to each bidder into
two parts: pitem bits to denote a bundle of items and pprice bits to denote bid
value (so p = pitem+pprice). Since qubits are expensive, a succinct represen-
tation of items is best. Depending on the type of auction, we have various
choices with different efficiency in using bits. For example, the pitem item
bits could indicate the item in the bid, allowing pitem qubits to specify up to
2pitem different items. Another case is multiple units of a single item, so pitem
could specify how many units a bidder wants (with the understanding the
bid is for all those units not a partial amount) so the bits could specify 2pitem
different numbers. In the general case, bids are on arbitrary sets of items or
bundles, and we represent a bundle with m bits, 1 if the corresponding item
is a part of the bundle and 0 otherwise, i.e., m = pitem. We focus on this
general case in the remainder of the section. Allowing bids on sets of items
is called a combinatorial auction [6].
With multiple items, the bid operator ψj = Ujψinit gives a superposition
of bids of the form
i , b
where b
i is bidder j’s bid for a bundle of
items I
. In this notation, the null bid is |∅, b〉, and the specified amount
b is irrelevant so we take it to be zero in the examples. A superposition
specifies a set of distinct bids, with at most one allowed to win.
Example 4. Consider a combinatorial auction with two items X, Y and
integer prices ranging from 0 to 3. With p = 4 bits for each bidder, using 2
bits each to specify item bundles and prices, is sufficient to specify the bids.
The full space for a bidder has dimension 2p = 16, consisting of 4 possible
item bundle choices and 4 price choices. Suppose a bidder places a bid
(|∅, 0〉+ |X, 1〉 + |(X,Y ), 2〉)
i.e., a bid of 1 for item X alone, and 2 for the bundle of both items. In this
case, the bidder is not interested in item Y by itself. The dimension of the
subspace of this bid is 3. Another example is the bid
(|∅, 0〉 + |X, 1〉 + |X, 3〉 + |(X,Y ), 4〉)
The dimension of the subspace is 4. This superposition has multiple bids on
the same item X.
This bidding language is both expressive and compact. For instance, a
superposition of bundles of items readily expresses exclusive-or preferences,
where a bidder wants at most one of the bundles. It is also compact because
superpositions allow the bidder to use exactly the same qubits to place
no bid (i.e., ∅) and to place all the exponential number of bundles in a
combinatorial auction.
An allocation, as defined in Sec. 3.1, is a list of bids, one from each
bidder. With multiple items, an allocation is feasible if the item sets are
pairwise disjoint. As in the single item case, we consider the allocation
when all item sets are empty as infeasible. The value of a feasible allocation
is the sum total of the bid values of the different bids in the allocation. The
number of feasible states is ((n + 1)m − 1)2npprice . This is because we can
assign m items among n bidders where all items need not be allocated in
(n+1)m ways. The factor n+1 allows for some items to remain unallocated.
Since the allocation when all bidders place the null bid is an infeasible state,
we subtract 1. Each bidder can specify 2pprice different prices for the bundle
giving 2npprice possible choices for n bidders. Note that the number of feasible
states for a single item, m = 1, is different from that in Sec. 5.2 because
here we have changed the bidding language to represent items also.
The null bid in our protocol simplifies the evaluation of allocations for
combinatorial auctions. To see this, consider a protocol without the null bid.
In a single item case, F (x) for any allocation vector x would be maximum of
the bids placed by the different bidders on the item, which is fairly easy to
compute. But in the case of multiple items, there could be several allocations
for a vector x. For example suppose Alice bids on the set {A,B} and Bob
bids on {B,C}. Without the null set then both bids appear in the same
state and have to be evaluated by F (x). The possible allocation to the
bidders are
1. none to either
2. {A,B} to Alice
3. {B,C} to Bob, and
4. {A,B} to Alice and {B,C} to Bob (which is infeasible)
F (x) will have to compute the maximum of the values in all these states.
This is computationally complex when there are many items. By contrast,
the bidding language with the null bid avoids this combinatorial evaluation
within the search function F (x).
As in the case of single item auctions, we restrict ourselves to a one-shot
sealed bid classical combinatorial auction that we implement in a quantum
setting. The total number of states is 2pn and the total number of single
bidder deviations states is n(2p − 1). These expressions are the same as the
single item case. The condition for all single-deviation states to be mapped
to infeasible allocations, resulting in no winner, is
2np − ((n + 1)m − 1)2npprice ≥ n(2p − 1) (13)
This condition holds for cases relevant for auctions as seen in the following
claim.
Claim 2. Eq. (13) is true for all integers m, pprice ≥ 1 and n ≥ 2.
Proof. Recall p = m + pprice. We prove a stronger condition for integers
n,m ≥ 2, i.e., there exists enough infeasible states to handle joint deviations
up to n − 1 bidders. The number of k-bidder deviation states is the same
as the single-item case, i.e., Eq. (8). Thus this stronger condition, with the
same right-hand side as Eq. (10), is
2np − ((n + 1)m − 1)2npprice ≥ 2np − 1− (2p − 1)n (14)
Hence Eq. (14) is true if
(2p − 1)n ≥ ((n+ 1)m − 1)2npprice
⇔ 2ppricen(2m − 2−pprice)n ≥ ((n+ 1)m − 1)2npprice
⇔ (2m − 2−pprice)n ≥ (n+ 1)m − 1
Since 2−pprice ≤ 1, Eq. (14) is true if
(2m − 1)n ≥ (n+ 1)m − 1
which is true if
(2m − 1)
m ≥ (n+ 1)
Let f(m) ≡ (2m − 1)
m and g(n) ≡ (n + 1)
n . We establish the required
inequality, f(m) ≥ g(n), by showing f(m) is increasing in m when m ≥ 2,
g(n) is decreasing in n when n ≥ 2 and noting f(2) = g(2) =
Taking the derivative of f(m) with respect to m, we get,
(2m − 1)
2m ln(2)
2m − 1
ln(2m − 1)
This is positive if and only if
2m − 1
log2(2
m − 1)
This is true because log2(2
m − 1) < log2(2m) = m and hence both fractions
in the expression are greater than 1. Thus, f(m) is increasing for all m ≥ 2.
Taking derivative of g(n) with respect to n, we get,
(n+ 1)
1 + n
− ln(1 + n)
This is negative if and only if
ln(1 + n)
1 + n
This is true for n ≥ 2. Thus g(n) is decreasing in n for n ≥ 2.
Thus we have shown that Eq. (13) is true for n,m ≥ 2. It can be easily
checked that Eq. (13), is not true for n = 1 and true when n = 2 and
m = 1.
Thus, if a classical combinatorial auction has a NE then the correspond-
ing quantum auction protocol also has a NE with respect to initial state
deviations. Also there is an ǫ-equilibrium of the same auction with an im-
perfect search using N search steps. Moreover, the stronger condition of
Eq. (14) shows that in auctions with at least two bidders (n > 1), there are
enough infeasible states to give no winner for any deviation of initial ampli-
tudes that up to n− 1 can produce. Thus no groups, up to size n− 1, can
collude to benefit from initial amplitude deviations in the quantum auction.
7 Applications of Quantum Auctions
Two properties of quantum information may provide benefits to auctioneers
and bidders: the ability to compactly express complicated combinations of
preferences via superpositions and entanglement and the destruction of the
quantum state upon measurement. This section describes some economic
scenarios that could benefit from these properties.
As one economic application, quantum auctions provide a natural way to
solve the allocative externality problem [18, 25]. In this situation, a bidder’s
value for an item depends on the items received by other bidders. For
example, consider companies bidding on a big government project requiring
multiple companies to work on different parts. Allocative externality refers
to the issue that the costs for a company which wins a contract for one part
depends on which other companies win other parts. So company A may
be willing to bid more aggressively if it knows that company B will work
on related parts. Multiple simultaneous auctions for separate parts will not
handle these interdependencies and thus will be inefficient. One possible
solution is to let companies form partnership bids. That is joint bids that
are accepted together or not at all. Quantum information processing allows
for a natural way of forming partnership bids via entanglement. With the
protocol described in Sec. 6, multiple bids can be entangled so they will
either all be accepted together or none will be. Furthermore, quantum
auctions may provide more flexibility with respect to information privacy of
partnership bids than classical methods.
Specifically, with multiple items, groups of bidders could select joint op-
erators on their combined qubits, allowing them to express joint constraints
(e.g., where they either all win their specified items or none of them do)
without any of the other bidders or auctioneer knowing this choice. The
bidders do so by creating an entangled state instead of the factored form
for their qubits. Thus employing quantum entanglement provides bidders a
natural way for expressing any allocative externality. This possibility shows
bidding languages based on qubits are highly expressive and compact be-
cause bidders can use the same bits to express their individual bids and joint
bids via entanglement.
Example 5. Alice and Bob could jointly form the state
(|∅, 0,∅, 0〉 + |IA, bA, IB , bB〉+ |IC , bC , ID, bD〉) (15)
to represent the bidders willing to pay bA and bB for items IA and IB, or
to pay bC and bD for items IC and ID, but they are not willing to buy other
combinations, such as IA for Alice and ID for Bob.
In this scenario, a direct representation of bids, i.e., without a null bid,
would not guarantee the joint preferences are satisfied for all entangled bid-
ders or none of them. That is, without null bids, the superposition could
not express the joint preference through entanglement.
A group of k bidders operating jointly on their qubits to form entangled
bids could also produce initial amplitudes involving up to k-bidder deviation
states. However the discussion with Eq. (14) on multiple item auctions shows
our protocol can handle all deviation states a group of up to n−1 bidders can
produce, i.e., by mapping them to infeasible outcomes. Thus the additional
expressivity used for joint bids does not introduce additional opportunities
for collusion to change the outcomes via initial amplitude selection.
A second economic application for quantum auctions arises from their
privacy guarantee for losing bids. This property is economically useful when
bidders have incentives to hide information. An example is a scenario in
which companies are bidding for government contracts year after year. A
company’s bid usually contains information about its cost structure. If there
is reasonable expectation that the losing bids will be revealed, a company
may want to bid less aggressively to reduce the amount of information passed
to its competition for use in future auctions. This will lead to a less efficient
auction than if bidders reveal their true values. In this situation, a privacy
guarantee on the losing bids enables bidders to bid with less inhibition.
More generally, this privacy issue is only relevant when there are additional
interactions between these companies after the auction is concluded, such as
future auctions or negotiations where participants may be at a disadvantage
if their values are known to others.
This strong privacy property is unique to quantum information process-
ing. Privacy can be enforced via cryptographic methods for multi-player
computation [13], and in an auction can keep losing bids secret [22]. How-
ever, the information on the bids, and the key to decrypt them, remains
after the auction completes. People who have access to the key may be
legally compelled to reveal the information or choose to sell it. So while
cryptography can be secured computationally, it cannot guarantee the in-
tegrity of the person(s) who have the means to decrypt the information.
On the other hand, the quantum method destroys losing bids during the
search for the winning one and it is physically impossible to reconstruct
the bids after the auction process. Similarly, some of the other properties
of quantum auctions, such as correlations for partnership bids, can be pro-
vided classically [19]. Moreover, quantum mechanisms are readily simulated
classically [27] (as long as they involve at most 20 to 30 qubits). However,
these classical approaches lack the information security of quantum states.
More study is needed to determine scenarios where the privacy property of
the quantum protocol is significant.
8 Discussion
This paper describes a quantum protocol for auctions, gives a game theory
analysis of some strategic issues the protocol raises and suggests economic
scenarios that could benefit from these auctions. These include the privacy
of bids and the possibility of addressing allocative externalities. The search
used in our protocol can use arbitrary criteria for evaluating allocations,
thereby implementing other types of auctions with quantum states. Thus
while we focus our attention on the first-price sealed-bid auction, the pro-
tocol is more general: it can implement other pricing and allocation rules,
as well as multiple-unit-multiple-item auctions with combinatorial bids. For
example we can use this protocol in a multiple stage, iterative auction. In
fact, the protocol supports general bidding languages.
Encoding bids in quantum states raises new game theory issues because
the bidders’ strategic choices include specifying amplitudes in the quantum
states. The auction is not only probabilistic, but the winning probability
is not just a function of the amount bid. Instead a bidder can change the
probability of winning by altering the amplitudes of the quantum states
encoding his bid. For example, in the context of the first-price sealed-bid
auction, the auction does not guarantee the allocation of the item to the
highest bidder.
We show that the correct design of the protocol can solve a specific
version of this incentive problem. The salient design feature is an incentive
compatible mechanism so that bidders do not want to cheat, as opposed to
an algorithmic secure protocol that prevents bidders from cheating. Thus,
our design is an example of a quantum algorithm, in this case adiabatic
search, tuned to improve incentive issues rather than the usual focus in
quantum information processing on computation or security properties of
algorithms.
In addition, we show that the Nash equilibrium of the corresponding
classical first-price sealed-bid auction is an ǫ-equilibrium of the quantum
auction and that ǫ converges to zero when the quantum search associated
with the protocol uses an increasing number of steps, under the conditions
listed in Theorem 1. This result is with respect to changes in the initial
state of the search. It remains to be seen whether other bidder strategies
give some unilateral benefit, requiring further adjustments to the auction
design.
There are multiple directions for future work. First, we plan a series
of human subject experiments on whether people can indeed bid effectively
in the simple quantum auction scenario described in this paper. As with
previous experiments with a quantum public goods mechanism [2], such ex-
periments are useful tests of the applicability of game theory in practice,
and also suggest useful training and decision support tools. In particular,
people’s behavior in a quantum auction could differ from game theory predic-
tions that people select a Nash equilibrium based on idealized assumptions
of human rationality and full ability to evaluate consequences of strategic
choices with uncertainty.
Second, we plan to extend studies of quantum auctions to more com-
plicated economic scenarios, such as one with allocative externality. Our
analysis considers a single auction. An interesting extension is to a series of
auctions for similar items. If auctions are repeated, the game theory anal-
ysis is more complicated [28]. In particular, privacy concerns become more
significant since information revealed by a bidder’s behavior in one auction
may benefit other bidders in later auctions.
The quantum auction destroys all information about the losing bids. As
a result, it is not possible to conduct after-the-fact audits to verify that
the auction has been conducted correctly. Is there a way to modify the
mechanism to enable audits while preserving some of the privacy guaran-
tees? Security is another interesting issue. For example, there may be third
parties, aside from the auctioneer and bidders who are interested in inter-
cepting and changing bits in transit. Auctioneers may have incentives to
detect a bidder’s bid or skew auction results. The question is whether we
can build security around the protocol to prevent or at least detect these
types of attacks.
Similarly, many economics issues surrounding the protocol remain to be
resolved. For example, people behave as if they are risk averse in auction
situations [5, 4] which can change the predictions of game theory. Another
issue arises from the possibility of multiple Nash equilibria. We have only
shown that the desirable outcome is an equilibrium. The quantum protocol
can also have other equilibria. Since the Nash equilibrium concept alone
does not indicate how people select one equilibrium over another, additional
study is needed to determine when the desirable outcome is likely to occur.
Our protocol makes only limited use of quantum states, in particular
encoding bids in the subspace selected by the bidders but not using the
amplitudes separately. Thus it would be interesting to examine extensions to
the protocol exploiting the wider range of options for bidders. For example,
a protocol might use amplitudes of superpositions to indicate a bidder’s
probabilistic preferences, say, as in constructing a portfolio of items with
various expected values and risks. Such portfolios could be useful if bidders
have some uncertainty in their values (e.g., in bidding for oil field exploration
rights) rather than the standard private value framework considered in this
paper, where bidders know their own values for the items. With uncertain
values, probabilistic bids could allow bidders to match their risk preferences
along with their value estimates within the auction process.
As a final note, the number of qubits necessary to conduct an auction is
small compared to the requirement of complex computations such as factor-
ing. For example, if each bidder uses 7 bits (corresponding to 27 or about
100 bid values) and there are 3 bidders, about 25 qubits are needed, consid-
erably less than thousands needed for factoring interesting-sized numbers.
Thus with the advancement of quantum information processing technologies,
economics mechanisms could be early feasible applications.
Acknowledgments
We have benefited from discussions with Raymond Beausoleil, Saikat Guha, Philip
Kuekes, Andrew Landahl and Tim Spiller. This work was supported by DARPA
funding via the Army Research Office contract #W911NF0530002 to Dr. Beau-
soleil. This paper does not necessarily reflect the position or the policy of the
Government funding agencies, and no official endorsement of the views contained
herein by the funding agencies should be inferred.
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	Introduction
	Quantum Auction Protocol
	Quantum Auction Implementation
	Creation and interpretation of quantum bids
	Distributed Search
	Strategies with Quantum Operators
	Selecting the Subspace
	Altering Initial Amplitudes
	Changing Operator During Search
	Quantum Auction Design
	Checking for the Null Set
	The First-Price Sealed-Bid Auction
	A Game Theory Approach to Auctions
	Behavior with Perfect Search
	Bounded Number of Search Steps
	Testing for Changed Operators During Search
	Assigning Eigenvalues to Subspaces
	An Example
	General Case
	Multiple Items and Combinatorial Auction
	Applications of Quantum Auctions
	Discussion

  We present a quantum auction protocol using superpositions to represent bids
and distributed search to identify the winner(s). Measuring the final quantum
state gives the auction outcome while simultaneously destroying the
superposition. Thus non-winning bids are never revealed. Participants can use
entanglement to arrange for correlations among their bids, with the assurance
that this entanglement is not observable by others. The protocol is useful for
information hiding applications, such as partnership bidding with allocative
externality or concerns about revealing bidding preferences. The protocol
applies to a variety of auction types, e.g., first or second price, and to
auctions involving either a single item or arbitrary bundles of items (i.e.,
combinatorial auctions). We analyze the game-theoretical behavior of the
quantum protocol for the simple case of a sealed-bid quantum, and show how a
suitably designed adiabatic search reduces the possibilities for bidders to
game the auction. This design illustrates how incentive rather that
computational constraints affect quantum algorithm choices.

Introduction
Quantum information processing [23] offers potential improvements in a va-
riety of applications. Computational advantages [26, 14] of quantum com-
puters with many qubits have received the most attention but are difficult to
implement physically. On the other hand, technology for manipulating and
communicating just a few qubits could be sufficient to create new economic
mechanisms by altering the information security and strategic incentives of
the underlying game.
Examples of quantum mechanisms include the prisoner’s dilemma [10,
11, 7, 8], coordination [17, 21] and public goods provisioning [3]. In partic-
ular, a quantum mechanism can significantly reduce the free-rider problem
without a third-party enforcer or repeated interactions, both in theory and
practice [2].
In this paper, we examine quantum mechanisms for another economic
scenario: resource allocation by auction [28]. While traditional auction
mechanisms can efficiently allocate resources in many cases, quantum auc-
tion protocols offer improvements in preserving privacy of the losing bids
and dealing with scenarios in which bidders care about what other bidders
win when multiple items are auctioned. Specifically, using quantum super-
positions to represent bids prevents the auctioneer and other bidders from
viewing the bids during the auction without disrupting the auction process.
Furthermore, the auction result reveals nothing but the winning bid and
allocation.
The first part of the paper introduces a general quantum auction protocol
for various pricing and allocation rules, multiple unit auctions, combinatorial
auctions and partnership bids. For simplicity, we focus on the sealed-bid
first-price auction. In this auction, each bidder has one opportunity to
submit a bid. The winner is the highest bidder, who pays the amount bid
for the item. This auction has been well studied both theoretically [28] and
experimentally [5, 4], and contrasts with iterative auctions in which bidders
can incrementally increase their bids depending on how others bid.
If the auction is not well-matched to the bidders preferences, it can intro-
duce perverse incentives and result in poor outcomes, such as lost revenue
for the seller or economically inefficient allocations where items are not al-
located to those who value them most. Thus it is important to examine
incentives introduced with a proposed auction design. In particular, our
auction protocol involves quantum search, which introduces incentive issues
beyond those examined in prior quantum games [11].
A full analysis of incentive issues is complicated, even for classical auc-
tions. In this paper we focus on two incentive issues arising from the quan-
tum auction protocol. The first incentive issue arises from the possibility
of manipulating the search outcome by altering amplitudes associated with
different bids. We show how to revise an adiabatic search method to correct
this incentive problem, thereby preserving the classical Nash equilibrium.
From a quantum algorithm perspective, this construction of the search il-
lustrates how incentive issues affect algorithm design, in contrast to the
more common concern with computational efficiency in quantum informa-
tion processing.
Second, the quantum search for the highest bid is probabilistic, i.e., does
not always return the highest bid. While the probability of finding the
correct answer can be made as high as one wishes by using more iterations
of the search, the small residue probability of awarding the item to someone
other than the highest bidder may change bidding behavior. As a step
toward addressing the effect of probabilistic outcomes, we show that, with
sufficient steps in the quantum search, altering choices from those of the
corresponding deterministic auction gives at most a small improvement for
that bidder.
The paper is organized as follows. Sec. 2 describes the quantum auction
and the bidding language encoding bids in quantum states. Sec. 3 describes
the quantum search method to find the maximum bid. After these sections
describing the auction protocol, in Sec. 4 we turn to strategic issues raised
by the quantum nature of the auction beyond those in the corresponding
classical auctions. Then, in Sec. 5 we give a game theory analysis of some
of these strategic possibilities and describe how simple modifications of the
quantum search improves the auction outcome, in theory. Sec. 6 generalizes
the results to auctions of multiple items, including combinatorial auctions.
Sec. 7 describes scenarios for which the quantum protocol offers likely eco-
nomic advantages in terms of information security and ability to compactly
express complex dependencies among items and bidders. Finally, Sec. 8 sum-
marizes the quantum auction protocol and highlights a number of remaining
economic questions.
2 Quantum Auction Protocol
In our auction protocol, each bidder selects an operator that produces the de-
sired bid from a prespecified initial state. The auctioneer repeatedly asks the
bidders to apply their individual operators in a distributed implementation
of a quantum search to find the winning bid. More specifically, the quantum
auction protocol for sealed-bid auctions involves the following steps:
1. Auctioneer announces conventional aspects of the auction: type of
auction (e.g., first or second price and any reservation prices), the
good(s) for sale, the allowed price granularity (e.g., if bids can specify
values to the penny, or only to the dollar), and the criterion used to
determine the winner(s), e.g., maximizing revenue for the seller
2. Auctioneer announces how quantum states will be interpreted, i.e., as
specifying a price if only one good is for sale, or a combination of price
and a set of goods if combinations are for sale; and also announces
the initial quantum state. This state uses p qubits for each bidder.
Auctioneer announces the quantum search procedure.
3. Each bidder selects an operator on p qubits. Bidders keep their choice
of operator private.
4. Auctioneer produces a set of particles implementing p qubits for each
bidder, initializing the set to the announced initial state.
5. Auctioneer and bidders perform a distributed search for the winner
Fig. 1 illustrates this procedure for two bidders and repeating the steps
of the search twice. Realistic search involves a larger number of steps. In
contrast with other quantum games, e.g., public goods, that involve just
one round of interaction, the search required to identify the winners involves
multiple rounds of interaction among the participants. The required number
of iterations depends on the search method. In practice, the auctioneer could
pick the number of iterations based on prior experience with similar auctions,
or from simulating several test cases using valuations randomly drawn from
a plausible distribution of values for the auction items. Alternatively, the
auctioneer could repeat the procedure several times (possibly with steps
from each repetition interleaved in a random order) and use the best result
from these repetitions.
auctioneer
auctioneer
auctioneer
bidder 1 bidder 2
bidder 1 bidder 2
start
measure state
announce result
Figure 1: Schematic diagram of distributed search procedure, showing re-
peated interactions between auctioneer and bidders, in this case two bidders
and two steps of the distributed search.
number of bidders n
number of items in auction m
number of qubits per bidder p
state of qubits for bidder j ψj
state of all qubits Ψ = ψ1 ⊗ . . .⊗ ψn
Table 1: Notation for the quantum auction.
This auction protocol uses a distributed search so bidders’ operator
choices remain private. Specifically, the search operation requiring input
from the bidders is applied locally by each bidder, giving the overall opera-
U = U1 ⊗ U2 ⊗ . . .⊗ Un (1)
where n is the number of bidders and Ui the operator of bidder i.
3 Quantum Auction Implementation
A quantum auction requires finding the winning bid and corresponding bid-
der. This procedure has two components: the interpretation of the qubits
as bids, and the search procedure to find the winner. The following two sub-
sections discuss these components in the context of a single-item auction.
Sec. 6 generalizes this discussion to multiple items.
3.1 Creation and interpretation of quantum bids
We define a bid as the amount a bidder indicates he is willing to pay for
the item. An allocation is a list of bids, one from each bidder. The quan-
tum auction protocol manipulates superpositions of allocations. We use an
allocation rule to indicate how allocations specify a winner and amount paid.
Example 1. Consider an auction of one item with three bidders, willing to
pay $1, $3 and $10 for the item, respectively. We represent these bids as
|$1〉, |$3〉 and |$10〉, and the corresponding allocation as the product of these
states, i.e., |$1, $3, $10〉 with the ordering in the allocation understood to
correspond to the bidders. A simple allocation rule selects the highest bidder
as the winner, who pays the high bid. In this example, this rule results in
the third bidder winning, and paying $10 for the item.
Each bidder gets p qubits and can only operate on those bits. Thus each
bidder has 2p possible bid values, and can create superpositions of these
values. A superposition of bids specifies set of distinct bids, with at most
one allowed to win. The amplitudes of the superposition affect the likelihood
of various outcomes for the auction. For a single-item auction, a bidder
will typically have only one bid. As discussed below, more complicated
superpositions are useful for information hiding. Specifically, bidder j selects
an operator Uj on p qubits to apply to the initial state for that bidder’s
qubits ψinit specified by the auctioneer. The resulting state, ψj = Ujψinit,
is a superposition of bids, each of the form
where b
i is bidder j’s
bid for the item. The subscript i indicates one of the possible bids that can
be specified with p qubits according to the announced interpretation of the
bits.
We define the subspace used by bidder j as the set of states spanned by
the basis eigenvectors in ψj . Only these basis vectors appear in allocations
relevant for the search. As bidders apply their operators during the search,
the superposition of allocations remains within the subspace of each bidder.
In this case, where each bidder applies an operator only to their own qubits,
the superposition of allocations is always a factored form, i.e., Ψ = ψ1 ⊗
. . . ⊗ ψn. More generally, groups of bidders could operate jointly on their
qubits, entangling their bids in the allocations as discussed in Sec. 7.
To exploit information hiding properties of superpositions, the state re-
vealed at the end of the search should specify only the bidder who wins the
item and the corresponding bid. To achieve this, instead of a direct repre-
sentation of bids, we interpret bids formed from the p qubits available to a
bidder as containing a special null value, ∅, indicating a bid for nothing.
This null bid has additional benefits in multiple item settings, as discussed
in Sec. 6 and Sec. 7.
Example 2. Consider bidder j with two qubits and the initial state ψinit =
|00〉 corresponding to the vector (1, 0, 0, 0), which is interpreted as the null
bid. The other bid states are |01〉, |10〉 and |11〉 corresponding to vectors
(0,1,0,0), (0,0,1,0) and (0,0,0,1). These three states are interpreted as three
bid values in some preannounced way, e.g., $1, $2 and $3, respectively.
The operator
1 0 1 0
0 1 0 1
1 0 −1 0
0 1 0 −1
gives the initial state ψj = Ujψinit as (|00〉+|10〉)/
2 and specifies the search
subspace whose basis is the first and third columns of Uj in this example.
Thus the possible allocations involve only |00〉 and |10〉 for this bidder, cor-
responding to the null bid and a bid of $2, respectively.
In the presence of a null bid, we consider an allocation to be a feasible
if it contains exactly one bid not equal to ∅. The corresponding allocation
rule assigns no winner to infeasible allocations and, for feasible allocations,
the winner is the single bidder in the allocation whose bid is not ∅, and he
pays the amount bid. This allocation rule corresponds to a first-price single-
item auction, except there can be no winner, analogous to the situation in
auctions with a reservation price when no bidder exceeds that price.
3.2 Distributed Search
The auctioneer must find the best state according to an announced crite-
rion, e.g., maximum revenue. Specifically, the auctioneer has a evaluation
function F assigning a quality value to each allocation. The function F
assigns a lower value to infeasible allocations than to any feasible one. An
example is F equal to the revenue produced by the allocation (if feasible)
and otherwise is −1.
The auctioneer uses quantum search to find the allocation in the subspace
selected by the bidders giving the maximum value for F (e.g., a feasible
allocation giving the most revenue to the auctioneer). This could be done via
repeated uses of a decision-problem quantum search [14, 1] as a subroutine
within a search for the minimum threshold value of F giving a solution to the
decision problem, e.g., with a classical binary search on threshold values or
using results of prior iterations of the decision problem [9]. Alternatively, we
could use a method giving the maximum value directly (e.g., adiabatic [12]
if run for a sufficiently long time or heuristic methods [15, 16] based on some
prior knowledge of the distribution of bidders values). For definiteness, we
focus on the adiabatic method.
The adiabatic search is conventionally described as searching for the
minimum cost state. We use this convention by defining a state’s cost to be
the negative of the evaluation function F . The adiabatic search procedure,
if run sufficiently slowly, changes the initial superposition into a final super-
position in such a way that the amplitude in each initial eigenstate maps
to the same amplitude in the corresponding final eigenstate, up to a phase
factor (for nondegenerate eigenstates). We refer to this mapping of initial
to final eigenstates as a perfect search. In practice, with a finite time for
the search, there will be some transfer of amplitude among the eigenstates
so the search will not be perfect in the sense defined here. Instead the auc-
tion outcome is probabilistic: the auction will not always produce the best
outcome when starting from the ground state. For example, an auction in-
tending to find the highest bid could sometimes produce the second highest
bid instead. Conventionally, the search operations are chosen so the uniform
superposition is the lowest cost initial eigenstate. In our case, bidders are
free to choose their operators and need not create uniform superpositions.
A discrete implementation of adiabatic search consists of the following
steps:
• The auctioneer selects a number of search steps S and parameter ∆.
These need not be announced to the bidders.
• The auctioneer initializes the state of all np qubits to Ψinit = ψinit ⊗
. . . ⊗ ψinit = |0, . . . , 0〉, with n factors of ψinit in the product, and
ψinit = |0〉 is the initial state for the p qubits for a single bidder.
• The auctioneer sends these initialized qubits to the bidders who use
their individual operators and then return the qubits to the auctioneer,
jointly creating the state
Ψ0 = UΨinit (3)
• For s = 1, . . . , S, the auctioneer and bidders update the state to
Ψs = UD(f)U
†P (f)Ψs−1 (4)
with f = s/S the fraction of steps completed. The bid operator U and
its adjoint U † are performed by sending bits to the bidders as described
in Sec. 2. The diagonal matrices D(f) and P (f) are described below.
• The auctioneer measures the state ΨS, resulting in specific values for
all the bits, from which the winner and prices are determined by the
allocation rule described in Sec. 3.1.
The diagonal matrix P (f) adjusts the phases of the amplitudes according
to the cost associated with each allocation. In particular, using the cost
c(x) = −F (x) for allocation |x〉, we have
Pxx(f) = exp (−ifc(x)∆) (5)
Similarly, the diagonal matrix D(f) adjusts amplitude phases as defined by
a function d(x):
Dxx(f) = exp (−i(1− f)d(x)∆) (6)
The key property of d(x) is assigning the smallest value, e.g., 0, to |0〉,
thereby making the first column of U the ground state eigenvector. Aside
from this key property, the choice of d(x) is somewhat arbitrary. The con-
ventional choice in the adiabatic method uses the Hamming weight of the
state, i.e., d(x) equal to the number of 1 bits in the binary representation of
x. However, as described in Sec. 5, other choices for d(x) can improve the
incentive properties of the auction.
The discrete-step implementation of the continuous adiabatic method [12]
involves the limits ∆ → 0 and S∆ → ∞, in which case the final state ψS
has high probability to be the lowest cost state. In practice, this outcome
can often be achieved with considerably fewer steps using a fixed value of
∆, corresponding to a discrete version of the adiabatic method [16].
4 Strategies with Quantum Operators
Ideally, an auction achieves the economic objective of its design (e.g. maxi-
mum revenue for the seller). In practice, an auction design may not provide
incentives for participants to behave so as to achieve this objective. Usu-
ally auction designs are examined under the assumption of self-interested
rational participants. In conventional auctions, strategic issues include mis-
representation of the true value, collusion among bidders and false name
bidding (where a single bidder submits bids under several aliases). Some
of these issues can be addressed with suitable auction rules, e.g., second
price auctions encourage truthful reporting of values. Developing suitable
designs of classical auctions in a wide range of economic contexts remains a
challenging problem [28].
Quantum auctions raise strategic issues beyond those of classical auc-
tions. In our case, every step of the adiabatic search requires each bidder
to perform an operation on their qubits. Ideally, the bidder should use the
same operator U for creating ψinit as in every step of the search in Eq. (4). In
addition, bidders should include the null bid in their subspaces. In the clas-
sical first-price sealed-bid auction, the bidder makes one choice: the amount
to bid. In our quantum setting, this choice amounts to selecting the sub-
space to use with the quantum search. The remaining freedom to select U ,
and possibly a different U for each step in the search, are additional choices
provided by the quantum auction.
Bidders may be tempted to exploit the flexibility of choosing operators in
several general ways. First, they could use a subspace not including the null
bid. Second, they could use a different operator for creating ψinit than they
use in the rest of the search, thereby producing an altered initial amplitude
that is not the ground state eigenvector. Third they could change operators
during the search. If any such changes give significant probability for low
bids to win, bidders would be tempted to make such changes and include
a low bid in their subspace, hoping to profit significantly by winning the
auction with a low bid.
The remainder of this section describes some strategic issues unique to
quantum auctions and possible solutions. We further discuss a game theory
analysis of some of these issues in Sec. 5.
4.1 Selecting the Subspace
The use of the null bid in our protocol raises the strategic issue illustrated
in the following example:
Example 3. Consider an auction of a single item with two bidders Alice
and Bob. Using operators producing uniform amplitudes for the sake of
illustration, they ought to apply operators that create
(|∅〉+ |bA〉) and 1√
(|∅〉+ |bB〉)
respectively, where bA and bB are their desired bids. The initial superposition
for all the qubits is the product of these individual superpositions, i.e., Ψ0 is
(|∅,∅〉+ |bA,∅〉+ |∅, bB〉+ |bA, bB〉)
If bidders use these same operators during the search, the search algorithm
finds the highest revenue allocation, i.e., giving the item to the highest bid-
der. Suppose instead Bob picks an operator with a one-dimensional subspace,
producing an initial state |bB〉 rather than including ∅. The product super-
position is then
(|∅, bB〉+ |bA, bB〉)
Since the search remains in this subspace and the second allocation is infea-
sible, the search will return |∅, bB〉 no matter what Alice bids. Thus Bob
always wins the item, and can win using the lowest possible bid.
This example shows bidders have an incentive to exclude the null set
from their subspace. If all bidders make this choice, there will be no feasible
allocations in the joint subspace and the auction will always give no winner.
For auctions with more than two bidders, selecting subspaces excluding ∅ is
a weak Nash Equilibrium for the quantum auction because any other choice
by a single bidder still results in no feasible allocations.
4.2 Altering Initial Amplitudes
Strategic choices for bidders also arise from the search procedure itself, even
when using the correct subspace consisting of ∅ and the desired bid. In
particular, the probabilistic outcome of the search means the optimal bid
according to the auction criterion (e.g., highest revenue) will not always win.
For the adiabatic search method, bidders could try to arrange for especially
tiny eigenvalue gaps between the state corresponding to the best outcome
and another state allowing them to win with a low bid. A sufficiently small
gap could make the number of steps the auctioneer selects insufficient to give
the optimal state with high probability and instead give a significant chance
of producing the more favorable outcome. However, because the eigenvalues
are a complicated function of the operators of all bidders, and individual
bidders do not know the choices made by others, it will be difficult for a
bidder to determine how to make such especially small gaps and do so in a
way that gives a favorable outcome. Nevertheless, even fairly small proba-
bilities for not finding the optimal state could alter the strategic behavior
of the bidders.
A more direct way a bidder can arrange for a low bid to win is by altering
the initial state of the adiabatic search to start not in the ground state but
in an eigenvector corresponding to one of the first few eigenvalues above
the ground state. The adiabatic search takes such eigenvectors, with high
probability, to an outcome in which a bid lower than the highest wins. While
a single bidder cannot create an arbitrary initial condition, one bidder can
ensure that it is not the ground state. For example, a bidder could chose an
operator that gives a nonuniform amplitude for the initial state, in particular
(|∅〉−|bA〉)/
2, while using the uniform state (|∅〉+|bA〉)/
2 as the ground
state through the remainder of the search in Eq. (4). This can result in
significant probability for a low bid to win, and so a bidder is tempted to
deviate from the nominal operator choice.
Fig. 2 illustrates this behavior. Instead of starting in the ground state,
0 0.2 0.4 0.6 0.8 1
high bid wins
low bid wins
infeasible
Figure 2: Correspondence between initial basis and the possible allocations
for a single item auction with two bidders in the standard adiabatic search.
During the search, as f increases from 0 to 1, the eigenvalues of the four
states change as shown schematically in the figure. The states for f = 0
correspond to both bidders starting with the ground state, |00〉, the two
states obtained if one of the bidders starts with a different superposition,
|01〉 and |10〉 (“single-bidder deviation states”), and the state of both bidders
starting with different superpositions, |11〉 (“2-bidder deviation state”).
the bidder’s choice gives the initial state as a linear combination of the
ground state and the single-deviation state for that bidder, denoted as |01〉
or |10〉 for the two bidders in Fig. 2. Here a “single deviation” state is
one that a single bidder can create, i.e., by operating on just the qubits
available to that bidder. The adiabatic search splits the degeneracy, thereby
giving some probability for the lowest bid to win and some probability for
an infeasible allocation.
More generally, bidder i uses this strategy by selecting two different
operators U initi and Ui to use for forming the initial state and during the
search, respectively. These choices result in different joint operators, in
Eq. (1), used in Eq. (3) and (4).
As with selecting a subspace without ∅, if many or all bidders make
this choice, the initial state will have significant amplitude in eigenvectors
corresponding to large eigenvalues, which produce infeasible outcomes and
0 0.2 0.4 0.6 0.8 1
high bid wins
low bid wins
infeasible
Figure 3: Correspondence between the initial basis and the possible alloca-
tions for a single item auction with two bidders in the search with permuted
initial eigenvalues.
hence a high probability for no winner. Thus with standard adiabatic search,
if everyone uses the same operator for both initialization and search, then
each bidder is tempted to use a different initialization operator and bid low,
gaining a chance to win with a low bid. However, if multiple bidders attempt
this, the outcome will most likely be an infeasible state, with no winner.
We can address this problem by reordering the eigenvalues given by the
d(x) function in Eq. (6) so that any change in initial operator by a single
bidder increases probability of infeasible allocation but not the probability of
any feasible allocation with a bid lower than the highest bid. This is possible
because bidders only have access to their own bits, so can only form initial
superpositions from a limited set of basis vectors. Fig. 3 illustrates the
resulting situation. We give an analysis of this approach in Sec. 5.2.
4.3 Changing Operator During Search
The distributed search of Eq. (4) has each bidder using the same operator
for every step of the search. Thus bidders may gain some advantage by
altering their operator during the steps of the search. Gradually changing
the operator during the search amounts to a different path from initial to
final Hamiltonian during the adiabatic search. Thus, provided the auction-
eer uses enough steps, such changes will have at most a minor effect on the
outcome probabilities unless the bidder can arrange for particularly small
eigenvalue gaps among favorable states. Such arrangement is difficult, par-
ticularly since the bidder does not know the choices of other bidders and
the auctioneer could treat the bits from the bidders in an arbitrary, unan-
nounced order.
More significant changes in outcome is possible with sudden, large changes
in the operator during search. Since the use of bidders operators gradually
decreases during the search (i.e., Dxx(f) given in Eq. (6) approaches the
identity operator as f approaches 1), the most problematic situation is for
an abrupt change in operator at the beginning of the search. After such
a change, the adiabatic search continues its gradual change of states, but
now instead of starting in the ground state, it will instead have a linear
combination of various states obtained by mapping the original basis onto
the basis after the change.
5 Quantum Auction Design
In this section, we focus on mechanism design to reduce incentive issues
arising from the quantum aspects of the auction. We analyze incentive is-
sues with the Nash equilibrium (NE) concept commonly used to evaluate
auctions [28]. A given set of behaviors for the bidders is an equilibrium
if no single bidder can gain an advantage (i.e., higher expected payoff) by
switching to another behavior. Specifically, Sec. 5.1 describes an approach
to encouraging bidders to include the null set in their bids. In Sec. 5.2 we
show that using the ground state eigenvector is a NE provided bidders do
not change the operators during the search. Sec. 5.3 then discusses how
the auctioneer can discourage bidders from changing operators. Sec. 5.4 de-
scribes how the auction can be made symmetric across the different bidders.
We focus on single-item auctions in this section, but the ideas extend to
quantum combinatorial auctions, as described in Sec. 6.
5.1 Checking for the Null Set
One approach to the incentive to exclude the null set, described in Sec. 4.1,
is for the auctioneer to perform a second search: for the allocation with the
most ∅ values. This search uses the same distributed protocol of Eq. (4)
but with separate qubits and a different cost function to define P (f), i.e.,
setting c(x) to the number of non-∅ values in the allocation x. Interleaving
the additional search in a random order within the steps of the search for
the winning bid prevents bidders from knowing which search a given step
belongs to. So bidders could not consistently select different operators for
the two searches.
If all bidders include ∅ in their selected subspace, this additional search
returns |∅,∅, . . .〉. Any bidder found not to have included ∅ could be ex-
cluded from winning the auction. At this point the auctioneer could either
announce there is no winner, or restart the auction for the remaining bid-
ders without announcing this restart. The adiabatic search has a small but
nonzero probability of returning the wrong result, which would then incor-
rectly conclude some bidder did not include ∅. As long as the probability of
such errors is smaller than the error probability of the search for the winner,
these errors should not greatly affect the incentive structure of the mech-
anism. Alternatively, the auctioneer could use a search completing with
probability one in a finite number of steps, i.e., with different choices of D
and P in Eq. (4), the auctioneer could implement Grover’s algorithm [14]
to search for the allocation |∅,∅, . . .〉 in the joint subspace of the bidders.
Since the auctioneer does not know the size of the subspaces selected by the
bidders, the auctioneer would need to try various numbers of steps [1] before
concluding |∅,∅, . . .〉 is not in the selected subspaces. Unlike the adiabatic
search, failure would only indicate some bidder had not included ∅, but not
which one. Thus the auctioneer’s only alternative in this case is to announce
the auction has no winner.
While this approach removes the immediate benefit of not including the
null bid, its affect on broader strategic issues in the full auction is an open
question.
5.2 The First-Price Sealed-Bid Auction
In this section we examine the incentive structure of the auction with per-
muted eigenstates described in Sec. 4.2. We first review how game the-
ory applies to auctions. We then consider the quantum auction when the
search runs long enough to give successful completion almost always (“per-
fect search”). Finally, we consider the more realistic case of search with
small, but not negligible, probability for non-optimal outcomes.
5.2.1 A Game Theory Approach to Auctions
Game theory is a common approach to evaluating auctions [20, 28]. Con-
sider n people bidding for an item, with person i having value vi for the
item. Unlike discrete choice games, such as the prisoner’s dilemma, a strat-
egy for a private value auction involves a bidding function b(v), mapping
a bidder’s value to a corresponding bid. Theoretical analysis of auctions
usually involves identifying a NE strategy, if any. This is a strategy for all
players such that no bidder gains by changing this strategy given everyone
else is using it. This focus on possible changes by a single bidder assumes
bidders do not collude.
A primary issue for auction behavior is how much participants know
about other bidders’ values. Such knowledge can affect the choice of bid.
The most popular model of such knowledge is independent private values,
where the vi are independently drawn from the same distribution. Each
bidder knows his own value, but not the values of other bidders. However,
the distribution from which values come is common knowledge, i.e., known
to all bidders, each bidder knows the others know this fact, and so on. A
final ingredient for the analysis is an assumption of bidders’ goals. For
illustration, we use the common assumption that bidders are risk neutral
expected utility maximizers, and within the context of the auction, utility
is proportional to profit.
We illustrate this approach for a first-price sealed-bid auction, in which
each bidder submits a single bid without seeing any of the other bids. This
corresponds to the auction scenario considered in this paper. The bidder
with the highest bid gets the item and pays the amount of his bid. Thus if
bidder i bids bi, his profit is vi−bi if he wins the auction and zero otherwise.
To avoid possibly losing money, bidders should ensure bi ≤ vi, and bids are
required to be nonnegative.
In the symmetric case where bidders’ values all come from the same
distribution, a NE is a bidding function b(v). A bidder’s expected payoff
is (v − b(v))P (b) where v is his value, b is his bidding function and P (b)
is the probability of winning if he is using b(v) (which is also the function
others use in equilibrium). Let F be the cumulative distribution of values,
i.e., probability a value is at most v, and n be the number of bidders. The
equilibrium condition leads to a differential equation satisfied by b(v) [28].
As a simple example, when v is uniformly distributed between 0 and 1,
F (v) = v and the NE is b(v) = (n − 1)v/n. Thus, in the equilibrium
strategy, a bidder bids somewhat less than his value and the bid gets closer
to the value when there is more competition, i.e., larger n.
If bidders have differing value distributions, a NE involves a set of bidding
functions, {bi(v)}. An auction may have multiple equilibria.
5.2.2 Behavior with Perfect Search
With perfect search and non-colluding bidders, if bidders use the same opera-
tors for every step of the search, including initialization, and pick a subspace
with the null bid then the adiabatic search described in Sec. 3.2 finds the
highest revenue state. We now show that the auctioneer can choose eigenval-
ues for the search so that bidders have no incentive to create an initial state
different from the ground state. This choice corresponds to the auctioneer
selecting an appropriate function d(x) in Eq. (6).
Suppose bidder i uses operator Ui, giving the overall operator U with
Eq. (1). Suppose all bidders except bidder 1 use the same operator to create
the initial state as they use for the subsequent search. But bidder 1 uses two
operators: U init1 to form the initial state and U1 for the search. Thus the
initial state produced by bidder 1, ψ1 = U
1 ψinit, i.e., the first column of
U init1 , is not necessarily equal to the first column of U1 that bidder 1 uses for
the subsequent search. Instead, ψ1 may have contributions from all columns
of U1, i.e.,
αi |i〉 (7)
where |i〉 corresponds to column i, ranging from 0 to 2p−1, of U1. Combining
with the initial state of all other bidders, Eq. (3) gives Ψ0 =
i αi |i, 0, . . . , 0〉,
instead of the initial ground state |0, 0, . . . , 0〉.
Significantly, because a bidder can only operate on the p qubits from the
auctioneer and not on any of the qubits sent to other bidders, a single bidder
can only create a limited set of “single-deviation” initial states. In the case
of bidder 1, these states all have the form |i, 0, . . . , 0〉. Similarly, if bidder
j is the one using different initial and search operators, the states all have
the form |. . . , 0, i, 0, . . .〉, where only the jth position can be nonzero. Thus,
among the 2np basis states in the full search space, aside from the correct
ground state, only n(2p−1) are possible states some single bidder can create
when all other bidders use the same operator for initialization and search.
More generally, k bidders can create superpositions of (2p − 1)k basis
states in which none of them use the ground state initially, by selecting
different operators for initialization and search. Thus there are
(2p − 1)k (8)
k-deviation states that some set of k bidders can create, while the other
n− k bidders use the ground state.
Our formulation has n(2p−1) feasible allocations, i.e., situations in which
exactly one of the bidders has a non-∅ bid while all other bidders have ∅.
To see this, each of the n bidders could have the non-∅ bid, and this bid
could have any of 2p − 1 values (since the remaining value for the bidder’s
bits represents ∅). The remaining n− 1 bidders have only one choice each,
i.e., ∅.
Suppose the auctioneer selects d(x) such that d(|0, . . . , 0〉) = 0 is the
lowest eigenvalue and d(x) for all single-deviation states x is the largest
value, with intermediate values for all other states. Provided the number
of infeasible allocations is at least equal to the number of single-deviation
states, a perfect search will then map every single-deviation state to an
infeasible allocation, resulting in no winner for the auction. This condition
amounts to
2np − n(2p − 1) ≥ n(2p − 1) (9)
The following claim shows that Eq. (9) always is true in an auction scenario.
Claim 1. Eq. (9) is true for all integers n, p ≥ 1
Proof. When p = 1, Eq. (9) reduces to 2n−1 ≥ n, which is true for all n ≥ 1.
We prove a stronger condition for p ≥ 2, namely there are enough in-
feasible states to handle up to n − 1 bidders deviating. Using Eq. (8), this
stronger condition is
2np − n(2p − 1) ≥
(2p − 1) = 2np − 1− (2p − 1)n (10)
with the k = 1 term in the sum corresponding to the right-hand side of
Eq. (9). Writing x ≡ 2p − 1, Eq. (10) becomes f(x, n) ≡ xn − nx+ 1 ≥ 0.
Since p ≥ 2, we have x ≥ 3. For this range of x and for n ≥ 1, f(x, n) is
monotonically increasing in both arguments. To see f is monotonic for x, the
derivative of f(x, n) with respect to x is n(xn−1 − 1) which is nonnegative
since n ≥ 1 and x > 1. Similarly, the derivative with respect to n is
x(xn−1 ln(x) − 1) which is at least 3(ln(3) − 1) > 0 since n ≥ 1 and x ≥ 3.
Thus for the relevant range of n and x, f(x, n) ≥ f(3, 1) = 1 so Eq. (10) is
true for all n ≥ 1 and p ≥ 2.
Combining these cases for p = 1 and p ≥ 2 establishes the claim.
Using this claim, we demonstrate the permuted eigenvalue choices re-
move incentives to alter the initial amplitudes:
Theorem 1. If (a) auctioneer chooses eigenvalues as described above, (b)
{b∗i (v)}ni=1 is an equilibrium for the first-price classical auction, and (c) bid-
ders include the null set as part of their bids and use the same operator in
each step in the search except, possibly, for the initial state, then the strategy
of using bidding functions {b∗i (v)}ni=1 and the same operator for their initial
state as they use in the search is a NE for corresponding quantum auction.
Proof. Without loss of generality, suppose only bidder 1 deviates and all the
other bidders use {b∗i (v)}ni=2 and the same operator for initialization and
search. Then, as described above, the initial state Ψ0 is
i αi |i, 0, . . . , 0〉
for some choice of amplitudes αi, with i ranging from 0 to 2
p − 1.
A perfect adiabatic search maps each of these states to a corresponding
allocation. In particular, with d(|0, . . . , 0〉) having the smallest value of
the function d(x), the lowest cost allocation is produced with probability
|α0|2. This allocation corresponds to the highest bid winning. Moreover,
each |i, 0, . . . , 0〉 with i 6= 0 has the largest value of d(x), and so, because
of Eq. (9), maps to an infeasible allocation, giving no winner and hence no
value to bidder 1.
Hence the expected value for bidder 1 is |α0|2V where V is the value of
the expected profit of the corresponding classical auction to bidder 1. Since
|α0|2V ≤ V , bidder 1 cannot gain from such a deviation.
Furthermore, there is no gain from deviating from the bidding function
b∗1(v) since it will only decrease V , because, by assumption, {b∗i (v)}ni=1 is a
NE for the corresponding classical auction.
Because of Eq. (9), this discussion applies to deviations by any single
bidder, not just bidder 1. Thus, using bidding function {b∗i (v)}ni=1 and using
the same operator for their initial state as they use in the search is a NE.
The stronger condition, Eq. (10), shows that the number of infeasible
states is enough to give no winner for any choice of initial amplitudes that
up to n − 1 bidders can produce, provided p ≥ 2. Thus if an auctioneer
implements a collusion-proof classical auction with the quantum protocol
and assigns infeasible states as described then the resulting quantum auction
is collusion-proof up to n− 1 bidders for initial amplitude deviations.
The choice for d(x) satisfying the above requirements is not unique.
As one example, let x be the state index in the full search space, running
from 0 to 2np − 1. Consider x as written as a series of n base-2p numbers,
|x1, x2, . . . , xn〉. Define
d(x) = −r(x) (mod n+ 1) (11)
where r(x) is number of nonzero values among x1, x2, ..., xn. The mod oper-
ation gives all d(x) values in the range 0 to n. For the initial ground state,
x = |0, . . . , 0〉, r(x) = 0 so d(x) = 0, and this is the smallest possible value.
Single-deviation states have exactly one of the xi nonzero, giving r(x) = 1
and d(x) = n, the largest possible value. More generally, all k-deviation
states have r(x) = k so d(x) = n + 1 − k. This function definition gives
values directly from the representation of the state x, so, in particular, the
auctioneer can implement it without any knowledge of the subspaces selected
by the bidders.
The assumption of perfect search is a sufficient but not necessary con-
dition for the proof of Theorem 1. The necessary conditions are more com-
plicated because we only need that every single bidder deviation maps to a
linear combination of infeasible states. Thus mixing among different single-
deviation states during search (e.g., due to small eigenvalue gaps among
those states), or among states corresponding to two or more bidders deviat-
ing, does not affect the proof.
5.2.3 Bounded Number of Search Steps
Theorem 1 shows the quantum auction has the same NE as the classical
first price auction if the search is perfect and each bidder uses the same
operator for every search step of Eq. (4). Since adiabatic search, run for a
finite number of steps, is not perfect we examine the effect on the NE of
an imperfect search. We show that the NE for perfect search, i.e., bidding
as in a classical first price auction and using the same operator initially
and during the search, is an ǫ-equilibrium for the auction with imperfect
search. Furthermore, ǫ converges to zero as the number of search steps goes
to infinity. A strategic profile is an ǫ-equilibrium [24] if for every player,
the gains of unilateral defecting to another strategy is at most ǫ. This
weaker equilibrium concept is useful in our case because determining how to
exploit imperfect search is computationally difficult. Specifically, with the
small eigenvalue gaps and degeneracy it is hard to know whether imperfect
search benefits a particular bidder. Thus computational cost will likely
outweigh the small possible gain. In this situation, an ǫ-equilibrium is a
useful generalization of NE.
We must prove that for any ǫ there exists an N so that if the search
process uses at least N steps, the equilibrium of the game with a perfect
search is also an ǫ-equilibrium when using the actual search. To do so,
we bound the possible gain from deviation based on prior knowledge of the
range of possible bidder values. That is, we assume the distribution of values
has a finite upper bound v̄. In our context, one such bound is the maximum
bid value expressible by the announced interpretation of each bidders qubits.
Theorem 2. If the conditions of Theorem 1 are met, and assuming the pos-
sible bidder values are bounded by v̄, for any ǫ > 0, there exists an N so that
the NE in the quantum auction with a perfect search, shown in Theorem 1,
is also an ǫ-equilibrium of the same auction with an imperfect search using
N search steps.
Proof. Let ph be the probability of the highest bid wins. Let pinf be the
probability of reaching an infeasible state. Then po = 1 − ph − pinf is the
probability of a bid other than the highest bid wins.
With the adiabatic search, with nonzero eigenvalue gaps, the probability
of correctly mapping the initial to final states converges to one as the number
of search steps increases. Thus for any δ > 0, there always exists a N where
po is at most δ.
We define an equilibrium expected payoff function for bidder i with value
v as π∗i (v), when all bidders use their equilibrium bidding functions.
Without loss of generality, from the perspective of bidder i with value
v, the probability of achieving the equilibrium payoff, π∗i (v), if that bidder
does not deviate is 1 − δ. Thus the expected payoff of deviating is at most
πdeviatei (v) ≤ (1− δ)π∗i (v) + δv̄ because (a) the most any bidder can gain is
bounded by v̄, and (b) with probability 1− δ the auction either produces no
profit (pinf) or is identical to a classical auction (ph).
The expected gain g from deviating is the expected payoff from deviating
minus the expected payoff with no deviation, i.e., g = πdeviatei (v) − π∗i (v) ≤
δ(v̄ − π∗i (v)), which in turn is at most δv̄. Thus for any choice of δ, there
always exists an N where the maximum deviation benefit is at most δv̄.
For any ǫ > 0, using δ = ǫ/v̄ in the above discussion shows there always
exists an N where the deviation is at most ǫ.
5.3 Testing for Changed Operators During Search
One approach to the incentive issue of changing operators during search,
described in Sec. 4.3, is for the auctioneer to test the bidders by randomly
inserting additional probe steps in the search.
Specifically, suppose at any step of the search the auctioneer, with some
probability, decides to check a bidder by sending a new set of qubits in a
known state |φ〉, while storing the qubits for the search until a subsequent
step. For the test step, the auctioneer sets D or P to the identity operator.
The state returned by the bidder is then U ′iU
i |φ〉 or U
i Ui |φ〉, depending on
which part of the search step in Eq. (4) the auctioneer is testing. Without
loss of generality, we consider the former case.
Ideally, the bidder uses the same operator, so U ′i = Ui and U
i is the
identity. Suppose the test state is formed from some operator V , randomly
selected by the auctioneer, |φ〉 = V |0〉. If U ′iU
i is not the identity, the re-
turned state has the form α |φ〉+β |φ⊥〉, where |φ⊥〉 is some state orthogonal
to |φ〉 and |α|2 + |β|2 = 1. The auctioneer then applies V †, giving
α |0〉+ β |a〉 (12)
for some value a 6= 0. The auctioneer then measures this state, getting
0 with probability |α|2, indicating the bidder passes the test. Otherwise,
the auctioneer observes a different value, indicating the bidder changed the
operator.
Hence the chance of getting caught depends on how often the auctioneer
checks, and how big a change the bidder makes in the operator. Larger
operator changes are more likely to be caught. This testing behavior is
appropriate as small changes are not likely to have much affect on the search
outcome, and instead simply act as an alternate adiabatic path from initial
to final states. This technique is especially useful for risk averse bidders
since then even a small chance to be caught might be enough to prevent
bidders from wanting to change operators.
5.4 Assigning Eigenvalues to Subspaces
Quantum search acts on the full space of superpositions of the available
qubits, i.e., in our case to all 2np configurations of items and bids. In the
auction context, bidders choose operators to restrict the search to a subspace
of possible bids, namely the ones they wish to make. Conceptually, the
search described above is then restricted to the subspace selected by the
bidders.
The search can also be viewed as taking place in the full space of 2np
configurations. The operator U appearing in the search algorithm is block
diagonal (up to a permutation of the basis states), with only the block
operating on the selected subspace relevant for the search outcome. This
view of the search is that of the auctioneer, who has no prior knowledge
of the subspace selected by each bidder. The operator U is not known
to any single individual: instead its implementation is distributed among
the bidders, with each bidder implementing a part of the overall operator.
The auctioneer chooses the eigenvalues for the initial Hamiltonian and the
ordering for the qubits assigned to each bidder. These choices, which could
change during the search, affect the incentive structure of the auction as
described in Sec. 5.2.
This section describes how the auctioneer’s choice of d(x) can give the
same eigenvalues when restricted to the subspace actually selected for the
search. For simplicity, we suppose each bidder uses a 2-dimensional sub-
space, consisting of ∅ and the desired bid for the single item. While not
essential for the NE results discussed above, uniformity with respect to sub-
space choices means bidders are treated uniformly, so convergence of the
search is independent of the order in which the auctioneer considers the
bidders.
5.4.1 An Example
Consider n = 2 bidders, each with p = 2 bits, representing 4 values: ∅
and three bid values 1, 2, 3. A set of 2-bit operators to form a uniform
superposition of the form (|∅〉+ |b〉)/
2 where b is the bid value, 1, 2 or 3,
is 1/
2 times
1 −1 0 0
1 1 0 0
0 0 1 −1
0 0 1 1
1 0 −1 0
0 1 0 −1
1 0 1 0
0 1 0 1
1 0 0 −1
0 1 1 0
0 −1 1 0
1 0 0 1
which we can denote as A1, A2, A3, respectively, with the first columns giving
the uniform superposition of the three possible bid values. If the bidders
select bids b1, b2, respectively, the overall operator for the search is U =
Ab1 ⊗ Ab2 , used in Eq. (4) to perform each step of the search. Thus in this
case there are 9 possible subspaces the two bidders can jointly select. Up to
a permutation, U is block diagonal with the block containing the nonzero
entries of the first column, and hence all the nonzero amplitude during the
search, equal to
1 −1 −1 1
1 1 −1 −1
1 −1 1 −1
1 1 1 1
The search using U in the full 4-bit space is thus equivalent to one taking
place in the 2-bit subspace selected by the two bidders using this operator
The auctioneers’ choice of eigenvalues, i.e., the function d(x) used in
Eq. (6) should ensure the uniform superposition within the subspace defined
by the two bidders has the lowest value, say 0, and all other eigenstates have
larger values.
One possibility is the standard choice for the diagonal values d(x) when
searching in the full space of 24 states defined by the np = 4 bits, namely
the Hamming weight of each state, i.e., the number of 1 bits in its binary
representation, ranging from 0 to 4.
An alternative approach is picking d(x) so eigenvalues for the four states
appearing in V have the same values as they would have with using the
Hamming weight for a 2-bit search, ranging from 0 to 2. Doing so requires
selecting the eigenvalues to match the corresponding Hamming weights for
any choices the bidders make among A1, A2, A3. In this example, each bidder
has 2 qubits, so can represent 4 states, which we denote as |0〉 , . . . , |3〉. The
states for both bidders are products of these individual states, |0, 0〉 , . . . , |3, 3〉.
Examining the 9 possible cases for U , shows a consistent set of choices is
d(|x, y〉) equal to the number of nonzero values among x, y. With this d(x),
the adiabatic search in the subspace selected by the bidders is identical to
the standard adiabatic search for two bits. This choice treats both bidders
identically.
In this case we see the auctioneer can arrange the adiabatic search to
operate symmetrically no matter what choice of subspace each bidder makes
(i.e., no matter what value each bidder decides to bid). Thus from the point
of view of the bidders, the search, in effect, takes place within the subspace
of possible values defined by their bid selections.
5.4.2 General Case
For arbitrary numbers of bidders n and bits p, we consider a single-item
auction so each bidder would, ideally, pick an operator giving just two terms,
with b(j) the bid of bidder j for the single item and no bits needed to specify
which item the bidder is interested in. The choice of b(j) corresponds to
the bidder picking a 2-dimensional subspace of the 2p possible states. The
product of these subspaces gives a subspace S of all np qubits used in the
auction. The subspace S has dimension 2n and its states xS can be viewed
as strings of n bits. More specifically, we suppose bidder j implements the
operator Uj such that the rows and columns corresponding to ∅ and b
have nonzero values only for positions ∅ and b(j). That is, the elements of
Uj for these two values form a 2× 2 unitary matrix.
If the auctioneer knew the subspace S, the eigenvalue function d(x) used
in Eq. (6) could be selected to match any desired function dS(xS) of the
states in xS ∈ S. Without such knowledge, this is possible only for some
choices for dS .
Theorem 3. Provided dS(xS) depends only on the Hamming weight of the
states xS, a single choice of d(x) in the full space corresponds to dS(xS) in
all possible subspaces the bidders could select that include the null set.
Proof. Consider the full operator U given by Eq. (1). For the element Ux,y,
express the np bits defining the states x and y as sequences of p-bit values,
x1, . . . , xn and y1, . . . , yn, respectively, with each xi and yi between 0 and
2p − 1. From Eq. (1),
Ux,y =
(Ui)xi,yi
The matrix U is of size 2np × 2np while each Ui is of size 2p × 2p.
Consider the first column of U , i.e., y = 0. Ux,0 is nonzero only for those
x such that all the (Ui)xi,0 are nonzero. For this to be the case, each xi is
either 0 (corresponding to |∅〉 for that bidder’s superposition) or xi = b(i),
i.e., the bid value. Similarly, for all columns with each yi equal to 0 or b
These values for x, y are precisely the states in the selected subspace of the
bidders, S. For these choices of xi, yi, we can map 0 (i.e., p bits all equal to
zero) to the single bit 0, and each b(i) (specified by values for p bits) to the
single bit 1. This establishes a one-to-one mapping from states in the full
space, of np bits corresponding to the product of bidders’ superpositions,
to states in the subspace treated as n-bit vectors. Thus a function dS(xS)
applied to the subspace that depends on the Hamming weight, i.e., the
number of 1 bits in xS, is the same as a function on the full space depending
on the number of nonzero xi values in x = x1, . . . , xn.
We must show that a single choice of function d(x) in the full space
maps to the desired dS(xS) in any choice of bidder subspaces. To see this
is the case, consider any state in the full space x = x1, . . . , xn. Among
these xi, suppose h are nonzero, denoted by xa1 , . . . , xah . This state x
will appear in all selected subspaces in which bidder aj bids b
(aj) = xaj ,
for j = 1, . . . , h, and the remaining bidders have any choice of bid. That
is, x appears in (2p − 1)n−h possible subspaces S. Since x has exactly h
nonzero values, in each of these possible subspaces it maps to a state xS
with exactly h bits equal to 1, i.e., it has the same Hamming weight, h,
in all possible subspaces in which it appears. Thus any choice of function
dS(xS) depending only on the Hamming weight of xS will have the same
value in all these possible subspaces. This observation allows the auctioneer
to select that common value as the value for d(x), consistently giving the
desired eigenvalue function for any possible subspace. Since this holds for
all values of h, the auctioneer can operate in the full space with identical
search behavior no matter what subspace the bidders select.
For the auctioneer to operate without knowledge of the actual subspace
selected by the bidders and treat bidders identically, we need d(x) to map
to the same function on any subspace selected. In this case, the search
proceeds exactly as if the auctioneer did know the subspace choices made
by the bidders. The theorem gives one type of function for in which this is
the case. In particular, Eq. (11) is an example of a function satisfying this
theorem.
6 Multiple Items and Combinatorial Auction
While the paper focuses on the single item first-price sealed-bid auction, the
quantum protocol can apply to multiple items by changing the interpretation
of the bids, i.e., the bidding language. Such changes affect the counting of
deviation and feasible states, so we must check the validity of Theorem 1.
In the single item case, each bidder uses the p qubits to specify the bid
amount. With multiple items, the bid must specify both the items of interest
and a bid amount for the items. Various bidding languages can encode this
information.
For multiple items, we divide the p qubits allocated to each bidder into
two parts: pitem bits to denote a bundle of items and pprice bits to denote bid
value (so p = pitem+pprice). Since qubits are expensive, a succinct represen-
tation of items is best. Depending on the type of auction, we have various
choices with different efficiency in using bits. For example, the pitem item
bits could indicate the item in the bid, allowing pitem qubits to specify up to
2pitem different items. Another case is multiple units of a single item, so pitem
could specify how many units a bidder wants (with the understanding the
bid is for all those units not a partial amount) so the bits could specify 2pitem
different numbers. In the general case, bids are on arbitrary sets of items or
bundles, and we represent a bundle with m bits, 1 if the corresponding item
is a part of the bundle and 0 otherwise, i.e., m = pitem. We focus on this
general case in the remainder of the section. Allowing bids on sets of items
is called a combinatorial auction [6].
With multiple items, the bid operator ψj = Ujψinit gives a superposition
of bids of the form
i , b
where b
i is bidder j’s bid for a bundle of
items I
. In this notation, the null bid is |∅, b〉, and the specified amount
b is irrelevant so we take it to be zero in the examples. A superposition
specifies a set of distinct bids, with at most one allowed to win.
Example 4. Consider a combinatorial auction with two items X, Y and
integer prices ranging from 0 to 3. With p = 4 bits for each bidder, using 2
bits each to specify item bundles and prices, is sufficient to specify the bids.
The full space for a bidder has dimension 2p = 16, consisting of 4 possible
item bundle choices and 4 price choices. Suppose a bidder places a bid
(|∅, 0〉+ |X, 1〉 + |(X,Y ), 2〉)
i.e., a bid of 1 for item X alone, and 2 for the bundle of both items. In this
case, the bidder is not interested in item Y by itself. The dimension of the
subspace of this bid is 3. Another example is the bid
(|∅, 0〉 + |X, 1〉 + |X, 3〉 + |(X,Y ), 4〉)
The dimension of the subspace is 4. This superposition has multiple bids on
the same item X.
This bidding language is both expressive and compact. For instance, a
superposition of bundles of items readily expresses exclusive-or preferences,
where a bidder wants at most one of the bundles. It is also compact because
superpositions allow the bidder to use exactly the same qubits to place
no bid (i.e., ∅) and to place all the exponential number of bundles in a
combinatorial auction.
An allocation, as defined in Sec. 3.1, is a list of bids, one from each
bidder. With multiple items, an allocation is feasible if the item sets are
pairwise disjoint. As in the single item case, we consider the allocation
when all item sets are empty as infeasible. The value of a feasible allocation
is the sum total of the bid values of the different bids in the allocation. The
number of feasible states is ((n + 1)m − 1)2npprice . This is because we can
assign m items among n bidders where all items need not be allocated in
(n+1)m ways. The factor n+1 allows for some items to remain unallocated.
Since the allocation when all bidders place the null bid is an infeasible state,
we subtract 1. Each bidder can specify 2pprice different prices for the bundle
giving 2npprice possible choices for n bidders. Note that the number of feasible
states for a single item, m = 1, is different from that in Sec. 5.2 because
here we have changed the bidding language to represent items also.
The null bid in our protocol simplifies the evaluation of allocations for
combinatorial auctions. To see this, consider a protocol without the null bid.
In a single item case, F (x) for any allocation vector x would be maximum of
the bids placed by the different bidders on the item, which is fairly easy to
compute. But in the case of multiple items, there could be several allocations
for a vector x. For example suppose Alice bids on the set {A,B} and Bob
bids on {B,C}. Without the null set then both bids appear in the same
state and have to be evaluated by F (x). The possible allocation to the
bidders are
1. none to either
2. {A,B} to Alice
3. {B,C} to Bob, and
4. {A,B} to Alice and {B,C} to Bob (which is infeasible)
F (x) will have to compute the maximum of the values in all these states.
This is computationally complex when there are many items. By contrast,
the bidding language with the null bid avoids this combinatorial evaluation
within the search function F (x).
As in the case of single item auctions, we restrict ourselves to a one-shot
sealed bid classical combinatorial auction that we implement in a quantum
setting. The total number of states is 2pn and the total number of single
bidder deviations states is n(2p − 1). These expressions are the same as the
single item case. The condition for all single-deviation states to be mapped
to infeasible allocations, resulting in no winner, is
2np − ((n + 1)m − 1)2npprice ≥ n(2p − 1) (13)
This condition holds for cases relevant for auctions as seen in the following
claim.
Claim 2. Eq. (13) is true for all integers m, pprice ≥ 1 and n ≥ 2.
Proof. Recall p = m + pprice. We prove a stronger condition for integers
n,m ≥ 2, i.e., there exists enough infeasible states to handle joint deviations
up to n − 1 bidders. The number of k-bidder deviation states is the same
as the single-item case, i.e., Eq. (8). Thus this stronger condition, with the
same right-hand side as Eq. (10), is
2np − ((n + 1)m − 1)2npprice ≥ 2np − 1− (2p − 1)n (14)
Hence Eq. (14) is true if
(2p − 1)n ≥ ((n+ 1)m − 1)2npprice
⇔ 2ppricen(2m − 2−pprice)n ≥ ((n+ 1)m − 1)2npprice
⇔ (2m − 2−pprice)n ≥ (n+ 1)m − 1
Since 2−pprice ≤ 1, Eq. (14) is true if
(2m − 1)n ≥ (n+ 1)m − 1
which is true if
(2m − 1)
m ≥ (n+ 1)
Let f(m) ≡ (2m − 1)
m and g(n) ≡ (n + 1)
n . We establish the required
inequality, f(m) ≥ g(n), by showing f(m) is increasing in m when m ≥ 2,
g(n) is decreasing in n when n ≥ 2 and noting f(2) = g(2) =
Taking the derivative of f(m) with respect to m, we get,
(2m − 1)
2m ln(2)
2m − 1
ln(2m − 1)
This is positive if and only if
2m − 1
log2(2
m − 1)
This is true because log2(2
m − 1) < log2(2m) = m and hence both fractions
in the expression are greater than 1. Thus, f(m) is increasing for all m ≥ 2.
Taking derivative of g(n) with respect to n, we get,
(n+ 1)
1 + n
− ln(1 + n)
This is negative if and only if
ln(1 + n)
1 + n
This is true for n ≥ 2. Thus g(n) is decreasing in n for n ≥ 2.
Thus we have shown that Eq. (13) is true for n,m ≥ 2. It can be easily
checked that Eq. (13), is not true for n = 1 and true when n = 2 and
m = 1.
Thus, if a classical combinatorial auction has a NE then the correspond-
ing quantum auction protocol also has a NE with respect to initial state
deviations. Also there is an ǫ-equilibrium of the same auction with an im-
perfect search using N search steps. Moreover, the stronger condition of
Eq. (14) shows that in auctions with at least two bidders (n > 1), there are
enough infeasible states to give no winner for any deviation of initial ampli-
tudes that up to n− 1 can produce. Thus no groups, up to size n− 1, can
collude to benefit from initial amplitude deviations in the quantum auction.
7 Applications of Quantum Auctions
Two properties of quantum information may provide benefits to auctioneers
and bidders: the ability to compactly express complicated combinations of
preferences via superpositions and entanglement and the destruction of the
quantum state upon measurement. This section describes some economic
scenarios that could benefit from these properties.
As one economic application, quantum auctions provide a natural way to
solve the allocative externality problem [18, 25]. In this situation, a bidder’s
value for an item depends on the items received by other bidders. For
example, consider companies bidding on a big government project requiring
multiple companies to work on different parts. Allocative externality refers
to the issue that the costs for a company which wins a contract for one part
depends on which other companies win other parts. So company A may
be willing to bid more aggressively if it knows that company B will work
on related parts. Multiple simultaneous auctions for separate parts will not
handle these interdependencies and thus will be inefficient. One possible
solution is to let companies form partnership bids. That is joint bids that
are accepted together or not at all. Quantum information processing allows
for a natural way of forming partnership bids via entanglement. With the
protocol described in Sec. 6, multiple bids can be entangled so they will
either all be accepted together or none will be. Furthermore, quantum
auctions may provide more flexibility with respect to information privacy of
partnership bids than classical methods.
Specifically, with multiple items, groups of bidders could select joint op-
erators on their combined qubits, allowing them to express joint constraints
(e.g., where they either all win their specified items or none of them do)
without any of the other bidders or auctioneer knowing this choice. The
bidders do so by creating an entangled state instead of the factored form
for their qubits. Thus employing quantum entanglement provides bidders a
natural way for expressing any allocative externality. This possibility shows
bidding languages based on qubits are highly expressive and compact be-
cause bidders can use the same bits to express their individual bids and joint
bids via entanglement.
Example 5. Alice and Bob could jointly form the state
(|∅, 0,∅, 0〉 + |IA, bA, IB , bB〉+ |IC , bC , ID, bD〉) (15)
to represent the bidders willing to pay bA and bB for items IA and IB, or
to pay bC and bD for items IC and ID, but they are not willing to buy other
combinations, such as IA for Alice and ID for Bob.
In this scenario, a direct representation of bids, i.e., without a null bid,
would not guarantee the joint preferences are satisfied for all entangled bid-
ders or none of them. That is, without null bids, the superposition could
not express the joint preference through entanglement.
A group of k bidders operating jointly on their qubits to form entangled
bids could also produce initial amplitudes involving up to k-bidder deviation
states. However the discussion with Eq. (14) on multiple item auctions shows
our protocol can handle all deviation states a group of up to n−1 bidders can
produce, i.e., by mapping them to infeasible outcomes. Thus the additional
expressivity used for joint bids does not introduce additional opportunities
for collusion to change the outcomes via initial amplitude selection.
A second economic application for quantum auctions arises from their
privacy guarantee for losing bids. This property is economically useful when
bidders have incentives to hide information. An example is a scenario in
which companies are bidding for government contracts year after year. A
company’s bid usually contains information about its cost structure. If there
is reasonable expectation that the losing bids will be revealed, a company
may want to bid less aggressively to reduce the amount of information passed
to its competition for use in future auctions. This will lead to a less efficient
auction than if bidders reveal their true values. In this situation, a privacy
guarantee on the losing bids enables bidders to bid with less inhibition.
More generally, this privacy issue is only relevant when there are additional
interactions between these companies after the auction is concluded, such as
future auctions or negotiations where participants may be at a disadvantage
if their values are known to others.
This strong privacy property is unique to quantum information process-
ing. Privacy can be enforced via cryptographic methods for multi-player
computation [13], and in an auction can keep losing bids secret [22]. How-
ever, the information on the bids, and the key to decrypt them, remains
after the auction completes. People who have access to the key may be
legally compelled to reveal the information or choose to sell it. So while
cryptography can be secured computationally, it cannot guarantee the in-
tegrity of the person(s) who have the means to decrypt the information.
On the other hand, the quantum method destroys losing bids during the
search for the winning one and it is physically impossible to reconstruct
the bids after the auction process. Similarly, some of the other properties
of quantum auctions, such as correlations for partnership bids, can be pro-
vided classically [19]. Moreover, quantum mechanisms are readily simulated
classically [27] (as long as they involve at most 20 to 30 qubits). However,
these classical approaches lack the information security of quantum states.
More study is needed to determine scenarios where the privacy property of
the quantum protocol is significant.
8 Discussion
This paper describes a quantum protocol for auctions, gives a game theory
analysis of some strategic issues the protocol raises and suggests economic
scenarios that could benefit from these auctions. These include the privacy
of bids and the possibility of addressing allocative externalities. The search
used in our protocol can use arbitrary criteria for evaluating allocations,
thereby implementing other types of auctions with quantum states. Thus
while we focus our attention on the first-price sealed-bid auction, the pro-
tocol is more general: it can implement other pricing and allocation rules,
as well as multiple-unit-multiple-item auctions with combinatorial bids. For
example we can use this protocol in a multiple stage, iterative auction. In
fact, the protocol supports general bidding languages.
Encoding bids in quantum states raises new game theory issues because
the bidders’ strategic choices include specifying amplitudes in the quantum
states. The auction is not only probabilistic, but the winning probability
is not just a function of the amount bid. Instead a bidder can change the
probability of winning by altering the amplitudes of the quantum states
encoding his bid. For example, in the context of the first-price sealed-bid
auction, the auction does not guarantee the allocation of the item to the
highest bidder.
We show that the correct design of the protocol can solve a specific
version of this incentive problem. The salient design feature is an incentive
compatible mechanism so that bidders do not want to cheat, as opposed to
an algorithmic secure protocol that prevents bidders from cheating. Thus,
our design is an example of a quantum algorithm, in this case adiabatic
search, tuned to improve incentive issues rather than the usual focus in
quantum information processing on computation or security properties of
algorithms.
In addition, we show that the Nash equilibrium of the corresponding
classical first-price sealed-bid auction is an ǫ-equilibrium of the quantum
auction and that ǫ converges to zero when the quantum search associated
with the protocol uses an increasing number of steps, under the conditions
listed in Theorem 1. This result is with respect to changes in the initial
state of the search. It remains to be seen whether other bidder strategies
give some unilateral benefit, requiring further adjustments to the auction
design.
There are multiple directions for future work. First, we plan a series
of human subject experiments on whether people can indeed bid effectively
in the simple quantum auction scenario described in this paper. As with
previous experiments with a quantum public goods mechanism [2], such ex-
periments are useful tests of the applicability of game theory in practice,
and also suggest useful training and decision support tools. In particular,
people’s behavior in a quantum auction could differ from game theory predic-
tions that people select a Nash equilibrium based on idealized assumptions
of human rationality and full ability to evaluate consequences of strategic
choices with uncertainty.
Second, we plan to extend studies of quantum auctions to more com-
plicated economic scenarios, such as one with allocative externality. Our
analysis considers a single auction. An interesting extension is to a series of
auctions for similar items. If auctions are repeated, the game theory anal-
ysis is more complicated [28]. In particular, privacy concerns become more
significant since information revealed by a bidder’s behavior in one auction
may benefit other bidders in later auctions.
The quantum auction destroys all information about the losing bids. As
a result, it is not possible to conduct after-the-fact audits to verify that
the auction has been conducted correctly. Is there a way to modify the
mechanism to enable audits while preserving some of the privacy guaran-
tees? Security is another interesting issue. For example, there may be third
parties, aside from the auctioneer and bidders who are interested in inter-
cepting and changing bits in transit. Auctioneers may have incentives to
detect a bidder’s bid or skew auction results. The question is whether we
can build security around the protocol to prevent or at least detect these
types of attacks.
Similarly, many economics issues surrounding the protocol remain to be
resolved. For example, people behave as if they are risk averse in auction
situations [5, 4] which can change the predictions of game theory. Another
issue arises from the possibility of multiple Nash equilibria. We have only
shown that the desirable outcome is an equilibrium. The quantum protocol
can also have other equilibria. Since the Nash equilibrium concept alone
does not indicate how people select one equilibrium over another, additional
study is needed to determine when the desirable outcome is likely to occur.
Our protocol makes only limited use of quantum states, in particular
encoding bids in the subspace selected by the bidders but not using the
amplitudes separately. Thus it would be interesting to examine extensions to
the protocol exploiting the wider range of options for bidders. For example,
a protocol might use amplitudes of superpositions to indicate a bidder’s
probabilistic preferences, say, as in constructing a portfolio of items with
various expected values and risks. Such portfolios could be useful if bidders
have some uncertainty in their values (e.g., in bidding for oil field exploration
rights) rather than the standard private value framework considered in this
paper, where bidders know their own values for the items. With uncertain
values, probabilistic bids could allow bidders to match their risk preferences
along with their value estimates within the auction process.
As a final note, the number of qubits necessary to conduct an auction is
small compared to the requirement of complex computations such as factor-
ing. For example, if each bidder uses 7 bits (corresponding to 27 or about
100 bid values) and there are 3 bidders, about 25 qubits are needed, consid-
erably less than thousands needed for factoring interesting-sized numbers.
Thus with the advancement of quantum information processing technologies,
economics mechanisms could be early feasible applications.
Acknowledgments
We have benefited from discussions with Raymond Beausoleil, Saikat Guha, Philip
Kuekes, Andrew Landahl and Tim Spiller. This work was supported by DARPA
funding via the Army Research Office contract #W911NF0530002 to Dr. Beau-
soleil. This paper does not necessarily reflect the position or the policy of the
Government funding agencies, and no official endorsement of the views contained
herein by the funding agencies should be inferred.
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	Introduction
	Quantum Auction Protocol
	Quantum Auction Implementation
	Creation and interpretation of quantum bids
	Distributed Search
	Strategies with Quantum Operators
	Selecting the Subspace
	Altering Initial Amplitudes
	Changing Operator During Search
	Quantum Auction Design
	Checking for the Null Set
	The First-Price Sealed-Bid Auction
	A Game Theory Approach to Auctions
	Behavior with Perfect Search
	Bounded Number of Search Steps
	Testing for Changed Operators During Search
	Assigning Eigenvalues to Subspaces
	An Example
	General Case
	Multiple Items and Combinatorial Auction
	Applications of Quantum Auctions
	Discussion
