# This code is part of Qiskit.
#
# (C) Copyright IBM 2019, 2023.
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# that they have been altered from the originals.

r"""
================================
Operators (:mod:`qiskit.opflow`)
================================

.. currentmodule:: qiskit.opflow

.. deprecated:: 0.24.0

    The :mod:`qiskit.opflow` module is deprecated and will be removed no earlier
    than 3 months after the release date. For code migration guidelines,
    visit https://qisk.it/opflow_migration.

Operators and State functions are the building blocks of Quantum Algorithms.

A library for Quantum Algorithms & Applications is more than a collection of
algorithms wrapped in Python functions. It needs to provide tools to make writing
algorithms simple and easy. This is the layer of modules between the circuits and algorithms,
providing the language and computational primitives for QA&A research.

We call this layer the Operator Flow. It works by unifying computation with theory
through the common language of functions and operators, in a way which preserves physical
intuition and programming freedom. In the Operator Flow, we construct functions over binary
variables, manipulate those functions with operators, and evaluate properties of these functions
with measurements.

The Operator Flow is meant to serve as a lingua franca between the theory and implementation
of Quantum Algorithms & Applications. Meaning, the ultimate goal is that when theorists speak
their theory in the Operator Flow, they are speaking valid implementation, and when the engineers
speak their implementation in the Operator Flow, they are speaking valid physical formalism. To
be successful, it must be fast and physically formal enough for theorists to find it easier and
more natural than hacking Matlab or NumPy, and the engineers must find it straightforward enough
that they can learn it as a typical software library, and learn the physics naturally and
effortlessly as they learn the code. There can never be a point where we say "below this level
this is all hacked out, don't come down here, stay in the interface layer above." It all must
be clear and learnable.

Before getting into the details of the code, it's important to note that three mathematical
concepts unpin the Operator Flow. We derive most of the inspiration for the code structure from
`John Watrous's formalism <https://cs.uwaterloo.ca/~watrous/TQI/>`__ (but do not follow it exactly),
so it may be worthwhile to review Chapters I and II, which are free online, if you feel the
concepts are not clicking.

1. An n-qubit State function is a complex function over n binary variables, which we will
often refer to as *n-qubit binary strings*. For example, the traditional quantum "zero state" is
a 1-qubit state function, with a definition of f(0) = 1 and f(1) = 0.

2. An n-qubit Operator is a linear function taking n-qubit state functions to n-qubit state
functions. For example, the Pauli X Operator is defined by f(Zero) = One and f(One) = Zero.
Equivalently, an Operator can be defined as a complex function over two n-qubit binary strings,
and it is sometimes convenient to picture things this way. By this definition, our Pauli X can
be defined by its typical matrix elements, f(0, 0) = 0, f(1, 0) = 1, f(0, 1) = 1,
f(1, 1) = 0.

3. An n-qubit Measurement is a functional taking n-qubit State functions to complex values.
For example, a Pauli Z Measurement can be defined by f(Zero) = 0 and f(One) = 1.

.. note::

    While every effort has been made to make programming the Operator Flow similar to mathematical
    notation, in some places our hands are tied by the design of Python.  In particular, when using
    mathematical operators such as ``+`` and ``^`` (tensor product), beware that these follow
    `Python operator precedence rules
    <https://docs.python.org/3/reference/expressions.html#operator-precedence>`__.  For example,
    ``I^X + X^I`` will actually be interpreted as ``I ^ (X+X) ^ I == 2 * I^X^I``.  In these cases,
    you should use extra parentheses, like ``(I ^ X) + (X ^ I)``, or use the relevant method calls.

Below, you'll find a base class for all Operators, some convenience immutable global variables
which simplify Operator construction, and two groups of submodules: Operators and Converters.

Operator Base Class
===================

The OperatorBase serves as the base class for all Operators, State functions
and measurements, and enforces the presence and consistency of methods to manipulate these
objects conveniently.

.. autosummary::
   :toctree: ../stubs/
   :template: autosummary/class_no_inherited_members.rst

   OperatorBase

.. _operator_globals:

Operator Globals
================

The :mod:`operator_globals` is a set of immutable Operator instances that are
convenient building blocks to reach for while working with the Operator flow.

One qubit Pauli operators:
   :attr:`X`, :attr:`Y`, :attr:`Z`, :attr:`I`

Clifford+T, and some other common non-parameterized gates:
   :attr:`CX`, :attr:`S`, :attr:`H`, :attr:`T`, :attr:`Swap`, :attr:`CZ`

One qubit states:
   :attr:`Zero`, :attr:`One`, :attr:`Plus`, :attr:`Minus`

Submodules
==========

Operators
---------

The Operators submodules include the PrimitiveOp, ListOp, and StateFn class
groups which represent the primary Operator modules.

.. autosummary::
    :toctree: ../stubs/

    primitive_ops
    list_ops
    state_fns


Converters
----------

The Converter submodules include objects which manipulate Operators,
usually recursing over an Operator structure and changing certain Operators' representation.
For example, the :class:`~.expectations.PauliExpectation` traverses an Operator structure, and
replaces all of the :class:`~.state_fns.OperatorStateFn` measurements containing non-diagonal
Pauli terms into diagonalizing circuits following by :class:`~.state_fns.OperatorStateFn`
measurement containing only diagonal Paulis.

.. autosummary::
    :toctree: ../stubs/

    converters
    evolutions
    expectations
    gradients


Utility functions
=================

.. autofunction:: commutator
.. autofunction:: anti_commutator
.. autofunction:: double_commutator


Exceptions
==========

.. autoexception:: OpflowError
"""
import warnings

# New Operators
from .operator_base import OperatorBase
from .primitive_ops import (
    PrimitiveOp,
    PauliOp,
    MatrixOp,
    CircuitOp,
    PauliSumOp,
    TaperedPauliSumOp,
    Z2Symmetries,
)
from .state_fns import (
    StateFn,
    DictStateFn,
    VectorStateFn,
    CVaRMeasurement,
    CircuitStateFn,
    OperatorStateFn,
    SparseVectorStateFn,
)
from .list_ops import ListOp, SummedOp, ComposedOp, TensoredOp
from .converters import (
    ConverterBase,
    CircuitSampler,
    PauliBasisChange,
    DictToCircuitSum,
    AbelianGrouper,
    TwoQubitReduction,
)
from .expectations import (
    ExpectationBase,
    ExpectationFactory,
    PauliExpectation,
    MatrixExpectation,
    AerPauliExpectation,
    CVaRExpectation,
)
from .evolutions import (
    EvolutionBase,
    EvolutionFactory,
    EvolvedOp,
    PauliTrotterEvolution,
    MatrixEvolution,
    TrotterizationBase,
    TrotterizationFactory,
    Trotter,
    Suzuki,
    QDrift,
)
from .utils import commutator, anti_commutator, double_commutator

# Convenience immutable instances
from .operator_globals import (
    EVAL_SIG_DIGITS,
    X,
    Y,
    Z,
    I,
    CX,
    S,
    H,
    T,
    Swap,
    CZ,
    Zero,
    One,
    Plus,
    Minus,
)

# Gradients
from .gradients import (
    DerivativeBase,
    GradientBase,
    Gradient,
    NaturalGradient,
    HessianBase,
    Hessian,
    QFIBase,
    QFI,
    CircuitGradient,
    CircuitQFI,
)

# Exceptions
from .exceptions import OpflowError

__all__ = [
    # Operators
    "OperatorBase",
    "PrimitiveOp",
    "PauliOp",
    "MatrixOp",
    "CircuitOp",
    "PauliSumOp",
    "TaperedPauliSumOp",
    "StateFn",
    "DictStateFn",
    "VectorStateFn",
    "CircuitStateFn",
    "OperatorStateFn",
    "SparseVectorStateFn",
    "CVaRMeasurement",
    "ListOp",
    "SummedOp",
    "ComposedOp",
    "TensoredOp",
    # Converters
    "ConverterBase",
    "CircuitSampler",
    "AbelianGrouper",
    "DictToCircuitSum",
    "PauliBasisChange",
    "ExpectationBase",
    "ExpectationFactory",
    "PauliExpectation",
    "MatrixExpectation",
    "AerPauliExpectation",
    "CVaRExpectation",
    "EvolutionBase",
    "EvolvedOp",
    "EvolutionFactory",
    "PauliTrotterEvolution",
    "MatrixEvolution",
    "TrotterizationBase",
    "TrotterizationFactory",
    "Trotter",
    "Suzuki",
    "QDrift",
    "TwoQubitReduction",
    "Z2Symmetries",
    # Convenience immutable instances
    "X",
    "Y",
    "Z",
    "I",
    "CX",
    "S",
    "H",
    "T",
    "Swap",
    "CZ",
    "Zero",
    "One",
    "Plus",
    "Minus",
    # Gradients
    "DerivativeBase",
    "GradientBase",
    "Gradient",
    "NaturalGradient",
    "HessianBase",
    "Hessian",
    "QFIBase",
    "QFI",
    "OpflowError",
    # utils
    "commutator",
    "anti_commutator",
    "double_commutator",
]

warnings.warn(
    "The ``qiskit.opflow`` module is deprecated as of qiskit-terra 0.24.0. "
    "It will be removed no earlier than 3 months after the release date. "
    "For code migration guidelines, visit https://qisk.it/opflow_migration.",
    category=DeprecationWarning,
    stacklevel=2,
)