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Zhuoyang Ye
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[Test] Add test for density matrix measurement
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‎test/density/test_density_measure.py

+53-13
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"""
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MIT License
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Copyright (c) 2020-present TorchQuantum Authors
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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import torchquantum as tq
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import numpy as np
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import qiskit.circuit.library.standard_gates as qiskit_gate
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from qiskit.quantum_info import DensityMatrix as qiskitDensity
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from torchquantum.plugin import op_history2qiskit
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from qiskit import Aer, transpile
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import numpy as np
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from unittest import TestCase
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def test_measure():
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n_shots = 10000
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qdev = tq.NoiseDevice(n_wires=3, bsz=1, record_op=True)
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qdev.x(wires=2) # type: ignore
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qdev.x(wires=1) # type: ignore
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qdev.ry(wires=0, params=0.98) # type: ignore
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qdev.rx(wires=1, params=1.2) # type: ignore
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qdev.cnot(wires=[0, 2]) # type: ignore
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tq_counts = tq.measure(qdev, n_shots=n_shots)
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class density_measure_test(TestCase):
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def test_single_qubit_random_layer(self):
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return
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circ = op_history2qiskit(qdev.n_wires, qdev.op_history)
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circ.measure_all()
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simulator = Aer.get_backend("aer_simulator_density_matrix")
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circ = transpile(circ, simulator)
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qiskit_res = simulator.run(circ, shots=n_shots).result()
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qiskit_counts = qiskit_res.get_counts()
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def test_two_qubit_random_layer(self):
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return
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for k, v in tq_counts[0].items():
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# need to reverse the bitstring because qiskit is in little endian
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qiskit_ratio = qiskit_counts.get(k[::-1], 0) / n_shots
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tq_ratio = v / n_shots
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print(k, qiskit_ratio, tq_ratio)
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assert np.isclose(qiskit_ratio, tq_ratio, atol=0.1)
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print("tq.measure for density matrix test passed")
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def test_three_qubit_random_layer(self):
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return
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if __name__ == "__main__":
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import pdb
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def test_mixed_layer(self):
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return
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pdb.set_trace()
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test_measure()

‎test/density/test_density_op.py

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from random import randrange, uniform
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import qiskit.circuit.library as qiskit_library
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from qiskit.quantum_info import Operator
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RND_TIMES = 100
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‎test/density/test_eval_observable_density.py

+147
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"""
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MIT License
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Copyright (c) 2020-present TorchQuantum Authors
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
8+
in the Software without restriction, including without limitation the rights
9+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
10+
copies of the Software, and to permit persons to whom the Software is
11+
furnished to do so, subject to the following conditions:
12+
13+
The above copyright notice and this permission notice shall be included in all
14+
copies or substantial portions of the Software.
15+
16+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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from qiskit import QuantumCircuit
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import numpy as np
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import random
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from qiskit.opflow import StateFn, X, Y, Z, I
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import torchquantum as tq
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from torchquantum.measurement import expval_joint_analytical_density, expval_joint_sampling_density
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from torchquantum.plugin import op_history2qiskit
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from torchquantum.util import switch_little_big_endian_matrix
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import torch
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pauli_str_op_dict = {
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"X": X,
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"Y": Y,
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"Z": Z,
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"I": I,
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}
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def test_expval_observable():
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# seed = 0
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# random.seed(seed)
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# np.random.seed(seed)
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# torch.manual_seed(seed)
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for k in range(100):
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# print(k)
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n_wires = random.randint(1, 10)
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obs = random.choices(["X", "Y", "Z", "I"], k=n_wires)
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random_layer = tq.RandomLayer(n_ops=100, wires=list(range(n_wires)))
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qdev = tq.NoiseDevice(n_wires=n_wires, bsz=1, record_op=True)
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random_layer(qdev)
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expval_tq = expval_joint_analytical_density(qdev, observable="".join(obs))[0].item()
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expval_tq_sampling = expval_joint_sampling_density(
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qdev, observable="".join(obs), n_shots=100000
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)[0].item()
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qiskit_circ = op_history2qiskit(qdev.n_wires, qdev.op_history)
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operator = pauli_str_op_dict[obs[0]]
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for ob in obs[1:]:
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# note here the order is reversed because qiskit is in little endian
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operator = pauli_str_op_dict[ob] ^ operator
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rho = StateFn(qiskit_circ).to_density_matrix()
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#print("Rho:")
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#print(rho)
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rho_evaled = rho
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rho_tq = switch_little_big_endian_matrix(
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qdev.get_densities_2d().detach().numpy()
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)[0]
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assert np.allclose(rho_evaled, rho_tq, atol=1e-5)
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#print("RHO passed!")
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#print("rho_evaled.shape")
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#print(rho_evaled.shape)
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#print("operator.shape")
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#print(operator.to_matrix().shape)
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#operator.eval()
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expval_qiskit = np.trace(rho_evaled@operator.to_matrix())
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#print("TWO")
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#print(expval_tq, expval_qiskit)
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assert np.isclose(expval_tq, expval_qiskit, atol=1e-1)
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if (
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n_wires <= 3
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): # if too many wires, the stochastic method is not accurate due to limited shots
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assert np.isclose(expval_tq_sampling, expval_qiskit, atol=1e-2)
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print("expval observable test passed")
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def util0():
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"""from below we know that the Z ^ I means Z on qubit 1 and I on qubit 0"""
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qc = QuantumCircuit(2)
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qc.x(0)
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operator = Z ^ I
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psi = StateFn(qc)
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expectation_value = (~psi @ operator @ psi).eval()
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print(expectation_value.real)
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# result: 1.0, means measurement result is 0, so Z is on qubit 1
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operator = I ^ Z
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psi = StateFn(qc)
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expectation_value = (~psi @ operator @ psi).eval()
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print(expectation_value.real)
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# result: -1.0 means measurement result is 1, so Z is on qubit 0
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operator = I ^ I
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psi = StateFn(qc)
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expectation_value = (~psi @ operator @ psi).eval()
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print(expectation_value.real)
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operator = Z ^ Z
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psi = StateFn(qc)
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expectation_value = (~psi @ operator @ psi).eval()
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print(expectation_value.real)
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qc = QuantumCircuit(3)
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qc.x(0)
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operator = I ^ I ^ Z
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psi = StateFn(qc)
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expectation_value = (~psi @ operator @ psi).eval()
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print(expectation_value.real)
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if __name__ == "__main__":
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#import pdb
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#pdb.set_trace()
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util0()
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#test_expval_observable()

‎test/density/test_expval_joint_sampling_grouping_density.py

+62
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"""
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MIT License
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Copyright (c) 2020-present TorchQuantum Authors
5+
6+
Permission is hereby granted, free of charge, to any person obtaining a copy
7+
of this software and associated documentation files (the "Software"), to deal
8+
in the Software without restriction, including without limitation the rights
9+
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
10+
copies of the Software, and to permit persons to whom the Software is
11+
furnished to do so, subject to the following conditions:
12+
13+
The above copyright notice and this permission notice shall be included in all
14+
copies or substantial portions of the Software.
15+
16+
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
17+
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
18+
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
19+
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
20+
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
21+
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
22+
SOFTWARE.
23+
"""
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import torchquantum as tq
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from torchquantum.measurement import (
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expval_joint_analytical_density,
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expval_joint_sampling_grouping_density,
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)
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import numpy as np
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import random
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def test_expval_joint_sampling_grouping():
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n_obs = 20
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n_wires = 4
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obs_all = []
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for _ in range(n_obs):
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obs = random.choices(["X", "Y", "Z", "I"], k=n_wires)
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obs_all.append("".join(obs))
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obs_all = list(set(obs_all))
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random_layer = tq.RandomLayer(n_ops=100, wires=list(range(n_wires)))
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qdev = tq.NoiseDevice(n_wires=n_wires, bsz=1, record_op=True)
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random_layer(qdev)
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expval_ana = {}
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for obs in obs_all:
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expval_ana[obs] = expval_joint_analytical_density(qdev, observable=obs)[0].item()
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expval_sam = expval_joint_sampling_grouping_density(
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qdev, observables=obs_all, n_shots_per_group=1000000
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)
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for obs in obs_all:
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# assert
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assert np.isclose(expval_ana[obs], expval_sam[obs][0].item(), atol=1e-1)
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print(obs, expval_ana[obs], expval_sam[obs][0].item())
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if __name__ == "__main__":
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test_expval_joint_sampling_grouping()

‎test/density/test_noise_model.py

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