# Quellcode fÃ¼r qiskit.algorithms.phase_estimators.hamiltonian_phase_estimation

# This code is part of Qiskit.
#
# (C) Copyright IBM 2020, 2022.
#
# obtain a copy of this license in the LICENSE.txt file in the root directory
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.

"""Phase estimation for the spectrum of a Hamiltonian"""

from __future__ import annotations

import warnings

from qiskit import QuantumCircuit
from qiskit.utils import QuantumInstance
from qiskit.utils.deprecation import deprecate_arg
from qiskit.opflow import (
SummedOp,
PauliOp,
MatrixOp,
PauliSumOp,
StateFn,
EvolutionBase,
PauliTrotterEvolution,
I,
)
from qiskit.providers import Backend
from .phase_estimation import PhaseEstimation
from .hamiltonian_phase_estimation_result import HamiltonianPhaseEstimationResult
from .phase_estimation_scale import PhaseEstimationScale
from ...circuit.library import PauliEvolutionGate
from ...primitives import BaseSampler
from ...quantum_info import SparsePauliOp, Statevector, Pauli
from ...synthesis import EvolutionSynthesis

[Doku]class HamiltonianPhaseEstimation:
r"""Run the Quantum Phase Estimation algorithm to find the eigenvalues of a Hermitian operator.

This class is nearly the same as :class:~qiskit.algorithms.PhaseEstimation, differing only
in that the input in that class is a unitary operator, whereas here the input is a Hermitian
operator from which a unitary will be obtained by scaling and exponentiating. The scaling is
performed in order to prevent the phases from wrapping around :math:2\pi.
The problem of estimating eigenvalues :math:\lambda_j of the Hermitian operator
:math:H is solved by running a circuit representing

.. math::

\exp(i b H) |\psi\rangle = \sum_j \exp(i b \lambda_j) c_j |\lambda_j\rangle,

where the input state is

.. math::

|\psi\rangle = \sum_j c_j |\lambda_j\rangle,

and :math:\lambda_j are the eigenvalues of :math:H.

Here, :math:b is a scaling factor sufficiently large to map positive :math:\lambda to
:math:[0,\pi) and negative :math:\lambda to :math:[\pi,2\pi). Each time the circuit is
run, one measures a phase corresponding to :math:lambda_j with probability :math:|c_j|^2.

If :math:H is a Pauli sum, the bound :math:b is computed from the sum of the absolute
values of the coefficients of the terms. There is no way to reliably recover eigenvalues
from phases very near the endpoints of these intervals. Because of this you should be aware
that for degenerate cases, such as :math:H=Z, the eigenvalues :math:\pm 1 will be
mapped to the same phase, :math:\pi, and so cannot be distinguished. In this case, you need
to specify a larger bound as an argument to the method estimate.

This class uses and works together with :class:~qiskit.algorithms.PhaseEstimationScale to
manage scaling the Hamiltonian and the phases that are obtained by the QPE algorithm. This
includes setting, or computing, a bound on the eigenvalues of the operator, using this
bound to obtain a scale factor, scaling the operator, and shifting and scaling the measured
phases to recover the eigenvalues.

Note that, although we speak of "evolving" the state according the Hamiltonian, in the
present algorithm, we are not actually considering time evolution. Rather, the role of time is
played by the scaling factor, which is chosen to best extract the eigenvalues of the
Hamiltonian.

A few of the ideas in the algorithm may be found in Ref. [1].

**Reference:**

[1]: Quantum phase estimation of multiple eigenvalues for small-scale (noisy) experiments
T.E. O'Brien, B. Tarasinski, B.M. Terhal
arXiv:1809.09697 <https://arxiv.org/abs/1809.09697>_

"""

@deprecate_arg(
"quantum_instance",
"Instead, use the sampler argument. See https://qisk.it/algo_migration for a "
"migration guide."
),
since="0.24.0",
)
def __init__(
self,
num_evaluation_qubits: int,
quantum_instance: QuantumInstance | Backend | None = None,
sampler: BaseSampler | None = None,
) -> None:
r"""
Args:
num_evaluation_qubits: The number of qubits used in estimating the phase. The phase will
be estimated as a binary string with this many bits.
quantum_instance: Deprecated: The quantum instance on which
the circuit will be run.
sampler: The sampler primitive on which the circuit will be sampled.
"""
# Avoid double warning on deprecated used of quantum_instance.
with warnings.catch_warnings():
warnings.simplefilter("ignore", DeprecationWarning)
self._phase_estimation = PhaseEstimation(
num_evaluation_qubits=num_evaluation_qubits,
quantum_instance=quantum_instance,
sampler=sampler,
)

def _get_scale(self, hamiltonian, bound=None) -> PhaseEstimationScale:
if bound is None:
return PhaseEstimationScale.from_pauli_sum(hamiltonian)

return PhaseEstimationScale(bound)

def _get_unitary(
self, hamiltonian, pe_scale, evolution: EvolutionSynthesis | EvolutionBase
) -> QuantumCircuit:
"""Evolve the Hamiltonian to obtain a unitary.

Apply the scaling to the Hamiltonian that has been computed from an eigenvalue bound
and compute the unitary by applying the evolution object.
"""

if self._phase_estimation._sampler is not None:

evo = PauliEvolutionGate(hamiltonian, -pe_scale.scale, synthesis=evolution)
unitary = QuantumCircuit(evo.num_qubits)
unitary.append(evo, unitary.qubits)

return unitary.decompose().decompose()
else:
# scale so that phase does not wrap.
scaled_hamiltonian = -pe_scale.scale * hamiltonian
unitary = evolution.convert(scaled_hamiltonian.exp_i())
if not isinstance(unitary, QuantumCircuit):
unitary = unitary.to_circuit()

return unitary.decompose().decompose()

# Decomposing twice allows some 1Q Hamiltonians to give correct results
# when using MatrixEvolution(), that otherwise would give incorrect results.
# It does not break any others that we tested.

[Doku]    def estimate(
self,
hamiltonian: PauliOp | MatrixOp | SummedOp | Pauli | SparsePauliOp | PauliSumOp,
state_preparation: StateFn | QuantumCircuit | Statevector | None = None,
evolution: EvolutionSynthesis | EvolutionBase | None = None,
bound: float | None = None,
) -> HamiltonianPhaseEstimationResult:
"""Run the Hamiltonian phase estimation algorithm.

Args:
hamiltonian: A Hermitian operator. If the algorithm is used with a Sampler
primitive, the allowed types are Pauli, SparsePauliOp, and PauliSumOp.
If the algorithm is used with a QuantumInstance, PauliOp, MatrixOp,
PauliSumOp, and SummedOp types are allowed.
state_preparation: The StateFn to be prepared, whose eigenphase will be
measured. If this parameter is omitted, no preparation circuit will be run and
input state will be the all-zero state in the computational basis.
evolution: An evolution converter that generates a unitary from hamiltonian. If
None, then the default PauliTrotterEvolution is used.
bound: An upper bound on the absolute value of the eigenvalues of
hamiltonian. If omitted, then hamiltonian must be a Pauli sum, or a
PauliOp, in which case a bound will be computed. If hamiltonian
is a MatrixOp, then bound may not be None. The tighter the bound,
the higher the resolution of computed phases.

Returns:
HamiltonianPhaseEstimationResult instance containing the result of the estimation
and diagnostic information.

Raises:
TypeError: If evolution is not of type EvolutionSynthesis when a Sampler is
provided.
TypeError: If hamiltonian type is not Pauli or SparsePauliOp or
PauliSumOp when a Sampler is provided.
ValueError: If bound is None and hamiltonian is not a Pauli sum, i.e. a
PauliSumOp or a SummedOp whose terms are of type PauliOp.
TypeError: If evolution is not of type EvolutionBase when no Sampler is
provided.
"""
if self._phase_estimation._sampler is not None:
if evolution is not None and not isinstance(evolution, EvolutionSynthesis):
raise TypeError(f"Expecting type EvolutionSynthesis, got {type(evolution)}")
if not isinstance(hamiltonian, (Pauli, SparsePauliOp, PauliSumOp)):
raise TypeError(
f"Expecting Hamiltonian type Pauli, SparsePauliOp or PauliSumOp, "
f"got {type(hamiltonian)}."
)

if isinstance(state_preparation, Statevector):
circuit = QuantumCircuit(state_preparation.num_qubits)
circuit.prepare_state(state_preparation.data)
state_preparation = circuit
if isinstance(hamiltonian, PauliSumOp):
id_coefficient, hamiltonian_no_id = _remove_identity_pauli_sum_op(hamiltonian)
else:
id_coefficient = 0.0
hamiltonian_no_id = hamiltonian
pe_scale = self._get_scale(hamiltonian_no_id, bound)
unitary = self._get_unitary(hamiltonian_no_id, pe_scale, evolution)
else:
if evolution is None:
evolution = PauliTrotterEvolution()
elif not isinstance(evolution, EvolutionBase):
raise TypeError(f"Expecting type EvolutionBase, got {type(evolution)}")

if isinstance(hamiltonian, PauliSumOp):
hamiltonian = hamiltonian.to_pauli_op()
elif isinstance(hamiltonian, PauliOp):
hamiltonian = SummedOp([hamiltonian])

if isinstance(hamiltonian, SummedOp):
# remove identitiy terms
# The term prop to the identity is removed from hamiltonian.
# This is done for three reasons:
# 1. Work around an unknown bug that otherwise causes the energies to be wrong in some
#    cases.
# 2. Allow working with a simpler Hamiltonian, one with fewer terms.
# 3. Tighten the bound on the eigenvalues so that the spectrum is better resolved, i.e.
#   occupies more of the range of values representable by the qubit register.
# The coefficient of this term will be added to the eigenvalues.
id_coefficient, hamiltonian_no_id = _remove_identity(hamiltonian)
# get the rescaling object
pe_scale = self._get_scale(hamiltonian_no_id, bound)

# get the unitary
unitary = self._get_unitary(hamiltonian_no_id, pe_scale, evolution)

elif isinstance(hamiltonian, MatrixOp):
if bound is None:
raise ValueError("bound must be specified if Hermitian operator is MatrixOp")

# Do not subtract an identity term from the matrix, so do not compensate.
id_coefficient = 0.0
pe_scale = self._get_scale(hamiltonian, bound)
unitary = self._get_unitary(hamiltonian, pe_scale, evolution)
else:
raise TypeError(f"Hermitian operator of type {type(hamiltonian)} not supported.")

if state_preparation is not None and isinstance(state_preparation, StateFn):
state_preparation = state_preparation.to_circuit_op().to_circuit()
# run phase estimation
phase_estimation_result = self._phase_estimation.estimate(
unitary=unitary, state_preparation=state_preparation
)
return HamiltonianPhaseEstimationResult(
phase_estimation_result=phase_estimation_result,
id_coefficient=id_coefficient,
phase_estimation_scale=pe_scale,
)

def _remove_identity(pauli_sum: SummedOp):
"""Remove any identity operators from pauli_sum. Return
the sum of the coefficients of the identities and the new operator.
"""
idcoeff = 0.0
ops = []
for op in pauli_sum:
p = op.primitive
if p.x.any() or p.z.any():
ops.append(op)
else:
idcoeff += op.coeff

return idcoeff, SummedOp(ops)

def _remove_identity_pauli_sum_op(pauli_sum: PauliSumOp | SparsePauliOp):
"""Remove any identity operators from pauli_sum. Return
the sum of the coefficients of the identities and the new operator.
"""

def _get_identity(size):
identity = I
for _ in range(size - 1):
identity = identity ^ I
return identity

idcoeff = 0.0
if isinstance(pauli_sum, PauliSumOp):
for operator in pauli_sum:
if operator.primitive.paulis == ["I" * pauli_sum.num_qubits]:
idcoeff += operator.primitive.coeffs[0]
pauli_sum = pauli_sum - operator.primitive.coeffs[0] * _get_identity(
pauli_sum.num_qubits
)

return idcoeff, pauli_sum.reduce()