def create_random_isingop(nqubits, nterms, seed=None):
"""Generates random ising operator acting on nqubits with nterms for testing
purposes.
Args:
nqubits: integer
The number of qubits in the qubit operator
nterms: integer
The number of terms in the qubit operator
*** OPTIONAL ***
seed: integer
The see for the random number generator
Returns:
an Ising Operator (openfermion.IsingOperator) object
"""
NUM_QUBITS = range(0, nqubits)
if seed is not None:
random.seed(seed)
# Initialize empty qubit operator
isingop = IsingOperator()
# Loop over number of separate terms in qubit operator
for i in range(0, nterms):
# Choose number of paulis to measure in term
num_paulis = random.choice(range(nqubits + 1))
# Create empty list of qubits
qubits = []
# Create empty term
full_term = ""
# Loop over paulis
for j in range(0, num_paulis):
# Choose random qubit
qubit_index = random.choice(NUM_QUBITS)
while qubit_index in qubits:
# Ensure qubit not already being measured in this term
qubit_index = random.choice(NUM_QUBITS)
qubits.append(qubit_index)
# Choose pauli
pauli_gate = "Z"
# Construct string
full_term += pauli_gate + str(qubit_index) + " "
# Add full term to qubit operator
isingop += IsingOperator(full_term)
return isingop
def test_get_expectation_values_from_measurements(self):
# Given
measurements = Measurements([(0, 1, 0), (0, 1, 0), (0, 0, 0),
(1, 0, 0), (1, 1, 1)])
ising_operator = IsingOperator("10[] + [Z0 Z1] - 15[Z1 Z2]")
target_expectation_values = np.array([10, -0.2, -3])
target_correlations = np.array([[100, -2, -30], [-2, 1, -9],
[-30, -9, 225]])
denominator = len(measurements.bitstrings)
covariance_11 = (target_correlations[1, 1] -
target_expectation_values[1]**2) / denominator
covariance_12 = (target_correlations[1, 2] -
target_expectation_values[1] *
target_expectation_values[2]) / denominator
covariance_22 = (target_correlations[2, 2] -
target_expectation_values[2]**2) / denominator
target_covariances = np.array([
[0, 0, 0],
[0, covariance_11, covariance_12],
[0, covariance_12, covariance_22],
])
# When
expectation_values = measurements.get_expectation_values(
ising_operator, False)
# Then
np.testing.assert_allclose(expectation_values.values,
target_expectation_values)
assert len(expectation_values.correlations) == 1
np.testing.assert_allclose(expectation_values.correlations[0],
target_correlations)
assert len(expectation_values.estimator_covariances) == 1
np.testing.assert_allclose(expectation_values.estimator_covariances[0],
target_covariances)
def test_get_expectation_values(self):
# Given
circuit = Circuit(Program(H(0), CNOT(0, 1), CNOT(1, 2)))
qubit_operator = IsingOperator('[] + [Z0 Z1] + [Z0 Z2] ')
target_expectation_values = np.array([1, 1, 1])
# When
expectation_values = self.sampling_simulator.get_expectation_values(
circuit, qubit_operator)
# Then
np.testing.assert_array_equal(expectation_values.values,
target_expectation_values)
def test_get_expectation_values_from_measurements(self):
# Given
measurements = [(0, 1, 0), (0, 1, 0), (0, 0, 0), (0, 0, 0), (1, 1, 1)]
ising_operator = IsingOperator('10[] + [Z0 Z1] - 10[Z1 Z2]')
target_expectation_values = np.array([10, 0.2, -2])
# When
expectation_values = get_expectation_values_from_measurements(
measurements, ising_operator)
# Then
np.testing.assert_array_equal(expectation_values.values,
target_expectation_values)
def test_parities_io():
measurements = [(1, 0), (1, 0), (0, 1), (0, 0)]
op = IsingOperator("[Z0] + [Z1] + [Z0 Z1]")
parities = get_parities_from_measurements(measurements, op)
save_parities(parities, "parities.json")
loaded_parities = load_parities("parities.json")
assert np.allclose(parities.values, loaded_parities.values)
assert len(parities.correlations) == len(loaded_parities.correlations)
for i in range(len(parities.correlations)):
assert np.allclose(parities.correlations[i],
loaded_parities.correlations[i])
remove_file_if_exists("parities.json")
def test_parities_io(self):
measurements = [(1, 0), (1, 0), (0, 1), (0, 0)]
op = IsingOperator("[Z0] + [Z1] + [Z0 Z1]")
parities = get_parities_from_measurements(measurements, op)
save_parities(parities, "parities.json")
loaded_parities = load_parities("parities.json")
self.assertTrue(np.allclose(parities.values, loaded_parities.values))
self.assertEqual(len(parities.correlations), len(loaded_parities.correlations))
for i in range(len(parities.correlations)):
self.assertTrue(
np.allclose(parities.correlations[i], loaded_parities.correlations[i])
)
os.remove("parities.json")
