7567300363

Committed 18 Jan 2024 08:32AM UTC coverage: 99.83%. Remained the same

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Prepare for release 1.1.0 (#120)

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/examples/quickstart-estimator.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.

# mypy: disable-error-code="no-untyped-def"

"""Quickstart example on using the Estimator primitive.

This examples uses a variational quantum eigensolver (VQE) to find
the ground state energy of a Hamiltonian.
"""

from qiskit.circuit.library import TwoLocal
from qiskit.quantum_info import SparsePauliOp
from scipy.optimize import minimize

from qiskit_aqt_provider import AQTProvider
from qiskit_aqt_provider.primitives import AQTEstimator

# Select an execution backend
provider = AQTProvider("ACCESS_TOKEN")
backend = provider.get_backend("offline_simulator_no_noise")

# Instantiate an estimator on the execution backend
estimator = AQTEstimator(backend)

# Specify the problem Hamiltonian
hamiltonian = SparsePauliOp.from_list(
    [
        ("II", -1.052373245772859),
        ("IZ", 0.39793742484318045),
        ("ZI", -0.39793742484318045),
        ("ZZ", -0.01128010425623538),
        ("XX", 0.18093119978423156),
    ]
)

# Define the VQE Ansatz, initial point, and cost function
ansatz = TwoLocal(num_qubits=2, rotation_blocks="ry", entanglement_blocks="cz")
initial_point = initial_point = [0] * 8


def cost_function(params, ansatz, hamiltonian, estimator):
    """Cost function for the VQE.

    Return the estimated expectation value of the Hamiltonian
    on the state prepared by the Ansatz circuit.
    """
    return estimator.run(ansatz, hamiltonian, parameter_values=params).result().values[0]


# Run the VQE using the SciPy minimizer routine
result = minimize(
    cost_function, initial_point, args=(ansatz, hamiltonian, estimator), method="cobyla"
)

# Print the found minimum eigenvalue
print(result.fun)

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	# mypy: disable-error-code="no-untyped-def"
14
15	"""Quickstart example on using the Estimator primitive.	4✔
16
17	This examples uses a variational quantum eigensolver (VQE) to find
18	the ground state energy of a Hamiltonian.
19	"""
20
21	from qiskit.circuit.library import TwoLocal	4✔
22	from qiskit.quantum_info import SparsePauliOp	4✔
23	from scipy.optimize import minimize	4✔
24
25	from qiskit_aqt_provider import AQTProvider	4✔
26	from qiskit_aqt_provider.primitives import AQTEstimator	4✔
27
28	# Select an execution backend
29	provider = AQTProvider("ACCESS_TOKEN")	4✔
30	backend = provider.get_backend("offline_simulator_no_noise")	4✔
31
32	# Instantiate an estimator on the execution backend
33	estimator = AQTEstimator(backend)	4✔
34
35	# Specify the problem Hamiltonian
36	hamiltonian = SparsePauliOp.from_list(	4✔
37	[
38	("II", -1.052373245772859),
39	("IZ", 0.39793742484318045),
40	("ZI", -0.39793742484318045),
41	("ZZ", -0.01128010425623538),
42	("XX", 0.18093119978423156),
43	]
44	)
45
46	# Define the VQE Ansatz, initial point, and cost function
47	ansatz = TwoLocal(num_qubits=2, rotation_blocks="ry", entanglement_blocks="cz")	4✔
48	initial_point = initial_point = [0] * 8	4✔
49
50
51	def cost_function(params, ansatz, hamiltonian, estimator):	4✔
52	"""Cost function for the VQE.
53
54	Return the estimated expectation value of the Hamiltonian
55	on the state prepared by the Ansatz circuit.
56	"""
57	return estimator.run(ansatz, hamiltonian, parameter_values=params).result().values[0]	4✔
58
59
60	# Run the VQE using the SciPy minimizer routine
61	result = minimize(	4✔
62	cost_function, initial_point, args=(ansatz, hamiltonian, estimator), method="cobyla"
63	)
64
65	# Print the found minimum eigenvalue
66	print(result.fun)	4✔

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