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[pre-commit.ci] auto fixes from pre-commit.com hooks

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pre-commit-ci[bot]
2025-09-09 11:17:49 +00:00
parent 641277c62b
commit 064cff6b33
1 changed files with 28 additions and 21 deletions
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49
src/qibotn/backends/quimb.py
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@@ -1,16 +1,16 @@
import re
import warnings
from collections import Counter, defaultdict
import numpy as np
import quimb.tensor as qtn
import quimb as qu
import quimb.tensor as qtn
from qibo.backends import NumpyBackend
from qibo.config import raise_error
from qibo.result import QuantumState
from qibotn.backends.abstract import QibotnBackend
from qibotn.result import TensorNetworkResult
import warnings
class QuimbBackend(QibotnBackend, NumpyBackend):
@@ -48,7 +48,7 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
self.max_bond_dimension = max_bond_dimension
self.n_most_frequent_states = n_most_frequent_states
def setup_backend_specifics(self, qimb_backend="numpy", optimizer='auto-hq'):
def setup_backend_specifics(self, qimb_backend="numpy", optimizer="auto-hq"):
"""Setup backend specifics.
Args:
qimb_backend: str
@@ -119,9 +119,7 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
frequencies = Counter(circ_quimb.sample(nshots))
main_frequencies = {
state: count
for state, count in frequencies.most_common(
self.n_most_frequent_states
)
for state, count in frequencies.most_common(self.n_most_frequent_states)
}
computational_states = list(main_frequencies.keys())
amplitudes = {
@@ -134,7 +132,11 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
frequencies = None
measured_probabilities = None
statevector = circ_quimb.to_dense(backend=self.backend, optimize=self.optimizer) if return_array else None
statevector = (
circ_quimb.to_dense(backend=self.backend, optimize=self.optimizer)
if return_array
else None
)
return TensorNetworkResult(
nqubits=circuit.nqubits,
backend=self,
@@ -167,34 +169,39 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
# for computing them in a single contraction. This does not work with CircuitMPS for some now
# for Quimb 1.11.1
operators_list, sites_list, coeffs_list = self._qiboobs_to_quimbobs(observable)
sites_list_grouped, operators_list_grouped, coeffs_list_grouped = self._group_by_tuples(sites_list, operators_list, coeffs_list)
sites_list_grouped, operators_list_grouped, coeffs_list_grouped = (
self._group_by_tuples(sites_list, operators_list, coeffs_list)
)
if self.ansatz == "MPS":
if len(sites_list)-len(sites_list_grouped) > 10:
if len(sites_list) - len(sites_list_grouped) > 10:
warnings.warn(
"More than 10 local operators on the same sites are not being grouped as this is not compatible with CircuitMPS. Expected value computation can be more efficient without an MPS ansatz."
)
circ_ansatz = (qtn.circuit.CircuitMPS)
circ_ansatz = qtn.circuit.CircuitMPS
circ = circ_ansatz.from_openqasm2_str(circuit.to_qasm())
expectation_value = 0.0
for ops, sites, coeffs in zip(operators_list, sites_list, coeffs_list):
exp_values = circ.local_expectation(ops, where=sites, backend=self.backend, optimize=self.optimizer)
exp_values = circ.local_expectation(
ops, where=sites, backend=self.backend, optimize=self.optimizer
)
expectation_value += np.dot(coeffs, exp_values)
return np.real(expectation_value)
else:
circ_ansatz = qtn.circuit.Circuit
circ = circ_ansatz.from_openqasm2_str(circuit.to_qasm())
expectation_value = 0.0
for ops, sites, coeffs in zip(operators_list_grouped, sites_list_grouped, coeffs_list_grouped):
exp_values = circ.local_expectation(ops, where=sites, backend=self.backend, optimize=self.optimizer)
for ops, sites, coeffs in zip(
operators_list_grouped, sites_list_grouped, coeffs_list_grouped
):
exp_values = circ.local_expectation(
ops, where=sites, backend=self.backend, optimize=self.optimizer
)
expectation_value += np.dot(coeffs, exp_values)
return np.real(expectation_value)
def _qiboobs_to_quimbobs(
self,
hamiltonian
):
def _qiboobs_to_quimbobs(self, hamiltonian):
"""
Convert a Qibo SymbolicHamiltonian into a Quimb-compatible decomposition.
@@ -247,16 +254,16 @@ class QuimbBackend(QibotnBackend, NumpyBackend):
coeffs_list.append(coeff)
return operators_list, sites_list, coeffs_list
def _group_by_tuples(self, A, B, C):
"""
Groups the elements of B and C by the unique tuples in A.
Parameters:
A (list of tuples): key tuples (can contain duplicates)
B (list): values aligned with A
C (list): values aligned with A
Returns:
(A_new, B_new, C_new):
A_new: list of unique tuples