9255177207

Committed 27 May 2024 12:52PM UTC coverage: 99.736%. Remained the same

Build # 9255177207

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Prepare release 1.5.0 (#161)

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/examples/qaoa.py

# This code is part of Qiskit.
#
# (C) Copyright Alpine Quantum Technologies GmbH 2023
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# 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.

"""Trivial minimization example using a quantum approximate optimization algorithm (QAOA).

This is the same example as in vqe.py, but uses QAOA instead of VQE as solver.
"""

from typing import Final

import qiskit_algorithms
from qiskit.quantum_info import SparsePauliOp
from qiskit_algorithms.minimum_eigensolvers import QAOA
from qiskit_algorithms.optimizers import COBYLA

from qiskit_aqt_provider import AQTProvider
from qiskit_aqt_provider.primitives import AQTSampler

RANDOM_SEED: Final = 0

if __name__ == "__main__":
    backend = AQTProvider("token").get_backend("offline_simulator_no_noise")
    sampler = AQTSampler(backend)

    # fix the random seeds such that the example is reproducible
    qiskit_algorithms.utils.algorithm_globals.random_seed = RANDOM_SEED
    backend.simulator.options.seed_simulator = RANDOM_SEED

    # Hamiltonian: Ising model on two spin 1/2 without external field
    J = 1.23456789
    hamiltonian = SparsePauliOp.from_list([("ZZ", 3 * J)])

    # Find the ground-state energy with QAOA
    optimizer = COBYLA(maxiter=100, tol=0.01)
    qaoa = QAOA(sampler, optimizer)
    result = qaoa.compute_minimum_eigenvalue(operator=hamiltonian)
    assert result.eigenvalue is not None  # noqa: S101

    print(f"Optimizer run time: {result.optimizer_time:.2f} s")
    print("Cost function evaluations:", result.cost_function_evals)
    print("Deviation from expected ground-state energy:", abs(result.eigenvalue - (-3 * J)))

1	# This code is part of Qiskit.
2	#
3	# (C) Copyright Alpine Quantum Technologies GmbH 2023
4	#
5	# This code is licensed under the Apache License, Version 2.0. You may
6	# obtain a copy of this license in the LICENSE.txt file in the root directory
7	# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
8	#
9	# Any modifications or derivative works of this code must retain this
10	# copyright notice, and modified files need to carry a notice indicating
11	# that they have been altered from the originals.
12
13	"""Trivial minimization example using a quantum approximate optimization algorithm (QAOA).	4✔
14
15	This is the same example as in vqe.py, but uses QAOA instead of VQE as solver.
16	"""
17
18	from typing import Final	4✔
19
20	import qiskit_algorithms	4✔
21	from qiskit.quantum_info import SparsePauliOp	4✔
22	from qiskit_algorithms.minimum_eigensolvers import QAOA	4✔
23	from qiskit_algorithms.optimizers import COBYLA	4✔
24
25	from qiskit_aqt_provider import AQTProvider	4✔
26	from qiskit_aqt_provider.primitives import AQTSampler	4✔
27
28	RANDOM_SEED: Final = 0	4✔
29
30	if __name__ == "__main__":	4✔
31	backend = AQTProvider("token").get_backend("offline_simulator_no_noise")	4✔
32	sampler = AQTSampler(backend)	4✔
33
34	# fix the random seeds such that the example is reproducible
35	qiskit_algorithms.utils.algorithm_globals.random_seed = RANDOM_SEED	4✔
36	backend.simulator.options.seed_simulator = RANDOM_SEED	4✔
37
38	# Hamiltonian: Ising model on two spin 1/2 without external field
39	J = 1.23456789	4✔
40	hamiltonian = SparsePauliOp.from_list([("ZZ", 3 * J)])	4✔
41
42	# Find the ground-state energy with QAOA
43	optimizer = COBYLA(maxiter=100, tol=0.01)	4✔
44	qaoa = QAOA(sampler, optimizer)	4✔
45	result = qaoa.compute_minimum_eigenvalue(operator=hamiltonian)	4✔
46	assert result.eigenvalue is not None # noqa: S101	4✔
47
48	print(f"Optimizer run time: {result.optimizer_time:.2f} s")	4✔
49	print("Cost function evaluations:", result.cost_function_evals)	4✔
50	print("Deviation from expected ground-state energy:", abs(result.eigenvalue - (-3 * J)))	4✔

qiskit-community / qiskit-aqt-provider / 9255177207

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