def test_channel_process_fidelity(self):
"""Test the process_fidelity function for channel inputs"""
depol = Choi(np.eye(4) / 2)
iden = Choi(Operator.from_label('I'))
# Completely depolarizing channel
f_pro = process_fidelity(depol, require_cp=True, require_tp=True)
self.assertAlmostEqual(f_pro, 0.25, places=7)
# Identity
f_pro = process_fidelity(iden, require_cp=True, require_tp=True)
self.assertAlmostEqual(f_pro, 1.0, places=7)
# Depolarizing channel
prob = 0.3
chan = prob * depol + (1 - prob) * iden
f_pro = process_fidelity(chan, require_cp=True, require_tp=True)
f_target = prob * 0.25 + (1 - prob)
self.assertAlmostEqual(f_pro, f_target, places=7)
# Depolarizing channel
prob = 0.5
op = Operator.from_label('Y')
chan = (prob * depol + (1 - prob) * iden) @ op
f_pro = process_fidelity(chan, op, require_cp=True, require_tp=True)
target = prob * 0.25 + (1 - prob)
self.assertAlmostEqual(f_pro, target, places=7)
def test_operator_process_fidelity(self):
"""Test the process_fidelity function for operator inputs"""
# Orthogonal operator
op = Operator.from_label('X')
f_pro = process_fidelity(op, require_cp=True, require_tp=True)
self.assertAlmostEqual(f_pro, 0.0, places=7)
# Global phase operator
op1 = Operator.from_label('X')
op2 = -1j * op1
f_pro = process_fidelity(op1, op2, require_cp=True, require_tp=True)
self.assertAlmostEqual(f_pro, 1.0, places=7)
def test_approx_random_unitary_channel_2q(self):
noise = Kraus(random_unitary(4, seed=123))
for opstr in ['pauli']:
new_result = approximate_quantum_error(noise,
operator_string=opstr)
old_result = self.old_approximate_quantum_error(
noise, operator_string=opstr)
self.assertEqual(new_result, old_result)
for opstr in ['reset']:
new_result = approximate_quantum_error(noise,
operator_string=opstr)
old_result = self.old_approximate_quantum_error(
noise, operator_string=opstr)
self.assertGreaterEqual(process_fidelity(noise, new_result),
process_fidelity(noise, old_result))
def test_process_fidelity(self):
Unitary1 = Pauli(label='XI').to_matrix()
Unitary2 = np.kron(np.array([[0, 1], [1, 0]]), np.eye(2))
process_fidelity(Unitary1, Unitary2)
self.assertAlmostEqual(process_fidelity(Unitary1, Unitary2),
1.0,
places=7)
theta = 0.2
Unitary1 = expm(-1j * theta * Pauli(label='X').to_matrix() / 2)
Unitary2 = np.array([[np.cos(theta / 2), -1j * np.sin(theta / 2)],
[-1j * np.sin(theta / 2),
np.cos(theta / 2)]])
self.assertAlmostEqual(process_fidelity(Unitary1, Unitary2),
1.0,
places=7)
def test_full_qpt(self, num_qubits, fitter):
"""Test QPT experiment"""
backend = AerSimulator(seed_simulator=9000)
seed = 1234
f_threshold = 0.94
target = qi.random_unitary(2**num_qubits, seed=seed)
qstexp = ProcessTomography(target)
if fitter:
qstexp.analysis.set_options(fitter=fitter)
expdata = qstexp.run(backend)
results = expdata.analysis_results()
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(isinstance(state, qi.Choi),
msg="fitted state is not a Choi matrix")
# Check fit state fidelity
fid = filter_results(results, "process_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(state,
target,
require_tp=False,
require_cp=False)
self.assertAlmostEqual(fid,
target_fid,
places=6,
msg="result fidelity is incorrect")
def test_nontp_process_fidelity(self):
"""Test process_fidelity for non-TP channel"""
chan = 0.99 * Choi(Operator.from_label('X'))
fid = process_fidelity(chan)
self.assertLogs('qiskit.quantum_info.operators.measures',
level='WARNING')
self.assertAlmostEqual(fid, 0, places=15)
def local_fidelity_optimization(channel: Choi,
target_op: Operator) -> np.ndarray:
""" Find local operation parameters to improve fidelity.
Args:
channel: Estimated quantum channel of target operation.
target_op: Ideal target operator.
Returns:
Local operation parameters.
"""
def local_rotations_inv(theta1, phi1, lam1, theta2, phi2, lam2):
return Operator(U3Gate(-theta1, -lam1, -phi1)).\
expand(Operator(U3Gate(-theta2, -lam2, -phi2)))
def fidelity_objective(params):
local_l = local_rotations_inv(*params[:6])
local_r = local_rotations_inv(*params[6:])
opt_oper = local_l.compose(target_op).compose(local_r)
return 1 - qi.process_fidelity(channel, opt_oper)
def to_favg(val):
return (4 * val + 1) / 5
raw_fid = qi.process_fidelity(channel, target_op)
print('Original F_avg: %.5f' % to_favg(raw_fid))
res = opt.dual_annealing(fidelity_objective,
[(-np.pi, np.pi) for _ in range(12)])
print('Optimized F_avg: %.5f' % to_favg(1 - res.fun))
return res.x
def test_circuit_multi(self):
"""Test circuit multi regs declared at start."""
qreg0 = QuantumRegister(2, 'q0')
creg0 = ClassicalRegister(2, 'c0')
qreg1 = QuantumRegister(2, 'q1')
creg1 = ClassicalRegister(2, 'c1')
circ = QuantumCircuit(qreg0, qreg1)
circ.x(qreg0[1])
circ.x(qreg1[0])
meas = QuantumCircuit(qreg0, qreg1, creg0, creg1)
meas.measure(qreg0, creg0)
meas.measure(qreg1, creg1)
qc = circ + meas
backend_sim = BasicAer.get_backend('qasm_simulator')
result = execute(qc, backend_sim, seed_mapper=34342).result()
counts = result.get_counts(qc)
target = {'01 10': 1024}
backend_sim = BasicAer.get_backend('statevector_simulator')
result = execute(circ, backend_sim, seed_mapper=3438).result()
state = result.get_statevector(circ)
backend_sim = BasicAer.get_backend('unitary_simulator')
result = execute(circ, backend_sim, seed_mapper=3438).result()
unitary = result.get_unitary(circ)
self.assertEqual(counts, target)
self.assertAlmostEqual(state_fidelity(basis_state('0110', 4), state), 1.0, places=7)
self.assertAlmostEqual(process_fidelity(Pauli(label='IXXI').to_matrix(), unitary),
1.0, places=7)
def test_qpt_teleport(self):
"""Test subset state tomography generation"""
# NOTE: This test breaks transpiler. I think it is a bug with
# conditionals in Terra.
# Teleport qubit 0 -> 2
backend = AerSimulator(seed_simulator=9000)
exp = ProcessTomography(teleport_circuit(),
measurement_qubits=[2],
preparation_qubits=[0])
expdata = exp.run(backend, shots=10000)
results = expdata.analysis_results()
# Check result
f_threshold = 0.95
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(isinstance(state, qi.Choi),
msg="fitted state is not a Choi matrix")
# Manually check fidelity
fid = qi.process_fidelity(state, require_tp=False, require_cp=False)
self.assertGreater(fid,
f_threshold,
msg="fitted state fidelity is low")
def test_noncp_process_fidelity(self):
"""Test process_fidelity for non-CP channel"""
u1 = Operator.from_label('X')
u2 = Operator.from_label('Z')
chan = 1.01 * Choi(u1) - 0.01 * Choi(u2)
fid = process_fidelity(chan)
self.assertLogs('qiskit.quantum_info.operators.measures', level='WARNING')
self.assertAlmostEqual(fid, 0, places=15)
def test_approx_random_mixed_unitary_channel_2q(self):
noise1 = UnitaryGate(random_unitary(4, seed=123))
noise2 = UnitaryGate(random_unitary(4, seed=456))
noise = QuantumError([(noise1, 0.7), (noise2, 0.3)])
for opstr in ['pauli']:
new_result = approximate_quantum_error(noise,
operator_string=opstr)
old_result = self.old_approximate_quantum_error(
noise, operator_string=opstr)
self.assertEqual(new_result, old_result)
for opstr in ['reset']:
new_result = approximate_quantum_error(noise,
operator_string=opstr)
old_result = self.old_approximate_quantum_error(
noise, operator_string=opstr)
self.assertGreaterEqual(process_fidelity(noise, new_result),
process_fidelity(noise, old_result))
def test_mixed_batch_exp(self):
"""Test batch state and process tomography experiment"""
# Subsystem unitaries
state_op = qi.random_unitary(2, seed=321)
chan_op = qi.random_unitary(2, seed=123)
state_target = qi.Statevector(state_op.to_instruction())
chan_target = qi.Choi(chan_op.to_instruction())
state_exp = StateTomography(state_op)
chan_exp = ProcessTomography(chan_op)
batch_exp = BatchExperiment([state_exp, chan_exp])
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
par_data = batch_exp.run(backend)
self.assertExperimentDone(par_data)
f_threshold = 0.95
# Check state tomo results
state_results = par_data.child_data(0).analysis_results()
state = filter_results(state_results, "state").value
# Check fit state fidelity
state_fid = filter_results(state_results, "state_fidelity").value
self.assertGreater(state_fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.state_fidelity(state, state_target, validate=False)
self.assertAlmostEqual(state_fid,
target_fid,
places=6,
msg="result fidelity is incorrect")
# Check process tomo results
chan_results = par_data.child_data(1).analysis_results()
chan = filter_results(chan_results, "state").value
# Check fit process fidelity
chan_fid = filter_results(chan_results, "process_fidelity").value
self.assertGreater(chan_fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(chan,
chan_target,
require_cp=False,
require_tp=False)
self.assertAlmostEqual(chan_fid,
target_fid,
places=6,
msg="result fidelity is incorrect")
def test_batch_exp_with_measurement_qubits(self):
"""Test batch process tomography experiment with kwargs"""
seed = 1111
nq = 3
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Preparation circuit
circuit = QuantumCircuit(nq)
for i, op in enumerate(ops):
circuit.append(op, [i])
# Component experiments
exps = []
targets = []
for i in range(nq):
targets.append(ops[i])
exps.append(
ProcessTomography(circuit,
measurement_qubits=[i],
preparation_qubits=[i]))
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
batch_exp = BatchExperiment(exps)
batch_data = batch_exp.run(backend)
batch_data.block_for_results()
# Check target fidelity of component experiments
f_threshold = 0.95
for i in range(batch_exp.num_experiments):
results = batch_data.component_experiment_data(
i).analysis_results()
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(isinstance(state, qi.Choi),
msg="fitted state is not a Choi matrix")
# Check fit state fidelity
fid = filter_results(results, "process_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(state,
targets[i],
require_tp=False,
require_cp=False)
self.assertAlmostEqual(fid,
target_fid,
places=6,
msg="result fidelity is incorrect")
def test_full_exp_meas_prep_qubits(self, qubits):
"""Test subset state tomography generation"""
# Subsystem unitaries
seed = 1111
nq = 3
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Target state
target_circ = QuantumCircuit(len(qubits))
for i, qubit in enumerate(qubits):
target_circ.append(ops[qubit], [i])
target = qi.Operator(target_circ)
# Preparation circuit
circ = QuantumCircuit(nq)
for i, op in enumerate(ops):
circ.append(op, [i])
# Run
backend = AerSimulator(seed_simulator=9000)
exp = ProcessTomography(circ,
measurement_qubits=qubits,
preparation_qubits=qubits)
expdata = exp.run(backend)
self.assertExperimentDone(expdata)
results = expdata.analysis_results()
# Check result
f_threshold = 0.95
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(isinstance(state, qi.Choi),
msg="fitted state is not a Choi matrix")
# Check fit state fidelity
fid = filter_results(results, "process_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(state,
target,
require_tp=False,
require_cp=False)
self.assertAlmostEqual(fid,
target_fid,
places=6,
msg="result fidelity is incorrect")
def test_circuit_multi_case2(self):
"""Test circuit multi regs declared at start.
"""
qreg0 = QuantumRegister(2, 'q0')
creg0 = ClassicalRegister(2, 'c0')
qreg1 = QuantumRegister(2, 'q1')
creg1 = ClassicalRegister(2, 'c1')
circ2 = QuantumCircuit()
circ2.add_register(qreg0)
circ2.add_register(qreg1)
circ2.x(qreg0[1])
circ2.x(qreg1[0])
meas2 = QuantumCircuit()
meas2.add_register(qreg0)
meas2.add_register(qreg1)
meas2.add_register(creg0)
meas2.add_register(creg1)
meas2.measure(qreg0, creg0)
meas2.measure(qreg1, creg1)
qc2 = circ2 + meas2
backend_sim = Simulators.get_backend('statevector_simulator')
result = execute(circ2, backend_sim).result()
state = result.get_statevector(circ2)
backend_sim = Simulators.get_backend('qasm_simulator')
result = execute(qc2, backend_sim).result()
counts = result.get_counts(qc2)
backend_sim = Simulators.get_backend('unitary_simulator')
result = execute(circ2, backend_sim).result()
unitary = result.get_unitary(circ2)
target = {'01 10': 1024}
self.assertEqual(target, counts)
self.assertAlmostEqual(state_fidelity(basis_state('0110', 4), state),
1.0,
places=7)
self.assertAlmostEqual(process_fidelity(
Pauli(label='IXXI').to_matrix(), unitary),
1.0,
places=7)
def test_parallel_exp(self):
"""Test parallel process tomography experiment"""
# Subsystem unitaries
seed = 1221
nq = 4
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Component experiments
exps = []
targets = []
for i in range(nq):
exps.append(ProcessTomography(ops[i], qubits=[i]))
targets.append(ops[i])
# Run batch experiments
backend = AerSimulator(seed_simulator=9000)
par_exp = ParallelExperiment(exps)
par_data = par_exp.run(backend)
self.assertExperimentDone(par_data)
# Check target fidelity of component experiments
f_threshold = 0.95
for i in range(par_exp.num_experiments):
results = par_data.child_data(i).analysis_results()
# Check state is density matrix
state = filter_results(results, "state").value
self.assertTrue(isinstance(state, qi.Choi),
msg="fitted state is not a Choi matrix")
# Check fit state fidelity
fid = filter_results(results, "process_fidelity").value
self.assertGreater(fid, f_threshold, msg="fit fidelity is low")
# Manually check fidelity
target_fid = qi.process_fidelity(state,
targets[i],
require_tp=False,
require_cp=False)
self.assertAlmostEqual(fid,
target_fid,
places=6,
msg="result fidelity is incorrect")
def test_circuit_multi(self):
"""Test circuit multi regs declared at start."""
qreg0 = QuantumRegister(2, "q0")
creg0 = ClassicalRegister(2, "c0")
qreg1 = QuantumRegister(2, "q1")
creg1 = ClassicalRegister(2, "c1")
circ = QuantumCircuit(qreg0, qreg1, creg0, creg1)
circ.x(qreg0[1])
circ.x(qreg1[0])
meas = QuantumCircuit(qreg0, qreg1, creg0, creg1)
meas.measure(qreg0, creg0)
meas.measure(qreg1, creg1)
qc = circ.compose(meas)
backend_sim = BasicAer.get_backend("qasm_simulator")
result = execute(qc, backend_sim, seed_transpiler=34342).result()
counts = result.get_counts(qc)
target = {"01 10": 1024}
backend_sim = BasicAer.get_backend("statevector_simulator")
result = execute(circ, backend_sim, seed_transpiler=3438).result()
state = result.get_statevector(circ)
backend_sim = BasicAer.get_backend("unitary_simulator")
result = execute(circ, backend_sim, seed_transpiler=3438).result()
unitary = Operator(result.get_unitary(circ))
self.assertEqual(counts, target)
self.assertAlmostEqual(state_fidelity(Statevector.from_label("0110"),
state),
1.0,
places=7)
self.assertAlmostEqual(process_fidelity(Operator.from_label("IXXI"),
unitary),
1.0,
places=7)
def test_unitary(self):
"""Test unitary gate instruction"""
num_trials = 10
max_qubits = 3
# Test 1 to max_qubits for random n-qubit unitary gate
for i in range(max_qubits):
num_qubits = i + 1
unitary_init = Operator(np.eye(2 ** num_qubits))
qr = QuantumRegister(num_qubits, "qr")
for _ in range(num_trials):
# Create random unitary
unitary = random_unitary(2 ** num_qubits)
# Compute expected output state
unitary_target = unitary.dot(unitary_init)
# Simulate output on circuit
circuit = QuantumCircuit(qr)
circuit.unitary(unitary, qr)
job = execute(circuit, self.backend)
result = job.result()
unitary_out = Operator(result.get_unitary(0))
fidelity = process_fidelity(unitary_target, unitary_out)
self.assertGreater(fidelity, 0.999)
def test_exp_measurement_preparation_qubits(self, qubits):
"""Test subset measurement process tomography generation"""
# Subsystem unitaries
seed = 1111
nq = 3
ops = [qi.random_unitary(2, seed=seed + i) for i in range(nq)]
# Preparation circuit
circ = QuantumCircuit(nq)
for i, op in enumerate(ops):
circ.append(op, [i])
num_meas = len(qubits)
exp = ProcessTomography(circ,
measurement_qubits=qubits,
preparation_qubits=qubits)
tomo_circuits = exp.circuits()
# Check correct number of circuits are generated
size = 3**num_meas * 4**num_meas
self.assertEqual(len(tomo_circuits), size)
# Check circuit metadata is correct
for circ in tomo_circuits:
meta = circ.metadata
clbits = meta.get("clbits")
self.assertEqual(clbits,
list(range(num_meas)),
msg="metadata clbits is incorrect")
# Check experiment target metadata is correct
exp_meta = exp._metadata()
target_state = exp_meta.get("target")
target_circ = QuantumCircuit(num_meas)
for i, qubit in enumerate(qubits):
target_circ.append(ops[qubit], [i])
fid = qi.process_fidelity(target_state, qi.Operator(target_circ))
self.assertGreater(fid, 0.99, msg="target_state is incorrect")
def get_fidelity_with_qiskit(dag_cir, noisy_position):
cir = dag_2_circuit(dag_cir)
U = Operator(cir)
channel = get_error_channel(dag_cir, noisy_position, 0.001)
fide = process_fidelity(channel, U)
return fide
def fidelity_objective(params):
local_l = local_rotations_inv(*params[:6])
local_r = local_rotations_inv(*params[6:])
opt_oper = local_l.compose(target_op).compose(local_r)
return 1 - qi.process_fidelity(channel, opt_oper)
