def test_zz(self):
"""
Run the simulator with unitary noise.
Then verify that the calculated ZZ matches the zz parameter.
"""
num_of_gates = np.arange(0, 60, 10)
gate_time = 0.1
qubits = [0]
spectators = [1]
# Generate experiments
circs, xdata, osc_freq = zz_circuits(num_of_gates,
gate_time,
qubits,
spectators,
nosc=2)
# Set the simulator with ZZ
zz_expected = 0.1
zz_unitary = np.eye(4, dtype=complex)
zz_unitary[3, 3] = np.exp(1j * 2 * np.pi * zz_expected * gate_time)
error = coherent_unitary_error(zz_unitary)
noise_model = NoiseModel()
noise_model.add_nonlocal_quantum_error(error, 'id', [0], [0, 1])
# Run the simulator
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 100
# For demonstration purposes split the execution into two jobs
backend_result = qiskit.execute(circs,
backend,
shots=shots,
seed_simulator=SEED,
noise_model=noise_model,
optimization_level=0).result()
initial_a = 0.5
initial_c = 0.5
initial_f = osc_freq
initial_phi = 0.0
ZZFitter(backend_result,
xdata,
qubits,
spectators,
fit_p0=[initial_a, initial_f, initial_phi, initial_c],
fit_bounds=([-0.5, 0, -np.pi,
-0.5], [1.5, 2 * osc_freq, np.pi, 1.5]))
def zz_circuit_execution() -> Tuple[qiskit.result.Result,
np.array,
List[int],
List[int],
float,
float]:
"""
Create ZZ circuits and simulate them.
Returns:
* Backend result.
* xdata.
* Qubits for the ZZ measurement.
* Spectators.
* ZZ parameter that used in the circuit creation
* Frequency.
"""
num_of_gates = np.arange(0, 60, 10)
gate_time = 0.1
qubits = [0]
spectators = [1]
# Generate experiments
circs, xdata, omega = zz_circuits(num_of_gates,
gate_time, qubits,
spectators, nosc=2)
# Set the simulator with ZZ
zz_value = 0.1
zz_unitary = np.eye(4, dtype=complex)
zz_unitary[3, 3] = np.exp(1j*2*np.pi*zz_value*gate_time)
error = coherent_unitary_error(zz_unitary)
noise_model = NoiseModel()
noise_model.add_nonlocal_quantum_error(error, 'id', [0], [0, 1])
# Run the simulator
backend = qiskit.Aer.get_backend('qasm_simulator')
shots = 100
backend_result = qiskit.execute(circs, backend,
shots=shots,
seed_simulator=SEED,
noise_model=noise_model,
optimization_level=0).result()
return backend_result, xdata, qubits, spectators, zz_value, omega
# difference in frequency between experiments
number_of_gates = np.arange(0, 150, 5) # number of identity gates per circuit
# ?: how did we get 150?
gate_time = 0.1 # time of running on a single gate
# ?: what is the zz rate?
# Selects qubits whose ZZ will be measured
qubits = [0] # measuring qubit 0?
spectators = [1] # qubits to "flip the state" (i.e., measure energy shift
# between qubits and spectators)
# Generates experiments.
oscillations_count = 2
circuits, delay_times, oscillation_freq = zz_circuits(number_of_gates,
gate_time,
qubits, spectators,
nosc = oscillations_count)
## Splits circuits into multiple jobs, giving results to fitter as a list.
# Sets simulator with ZZ
unknown_factor = 0.02 # ?: what is this? ground state energy frequency (E=hw)?
time_evolver = np.exp(1j * 2 * np.pi * unknown_factor * gate_time)
zz_unitary = np.eye(4, dtype=complex)
zz_unitary[3,3] = time_evolver
error = coherent_unitary_error(zz_unitary) # for applying to qubits in noise
# model below
my_noise_model = NoiseModel()
error_instructions = 'id'
noise_qubits = qubits + spectators
my_noise_model.add_nonlocal_quantum_error(error, error_instructions, qubits,