def main():
circuit = QuantumCircuit(2, 2)
circuit.u2(0, math.pi, 0)
circuit.cx(0, 1)
circuit.measure_all()
circuit2 = QuantumCircuit(2, 2)
circuit2.h(0)
circuit2.cx(0, 1)
circuit2.measure_all()
print("Available QuaC backends:")
print(Quac.backends())
simulator = Quac.get_backend('generic_counts_simulator',
n_qubits=2,
max_shots=10000,
max_exp=75,
basis_gates=['u1', 'u2', 'u3', 'cx']) # generic backends require extra parameters
# Noise model with T1, T2, and measurement error terms
noise_model = QuacNoiseModel(
[1000, 1000],
[50000, 50000],
[np.eye(2), np.array([[0.95, 0.1], [0.05, 0.9]])],
None
)
# Execute the circuit on the QuaC simulator
job = execute([circuit, circuit2], simulator, shots=1000, quac_noise_model=noise_model)
print(job.result())
print(job.result().get_counts())
plot_histogram(job.result().get_counts(), title="Bell States Example")
plt.show()
def main():
circuit1 = QuantumCircuit(5, 5)
circuit2 = QuantumCircuit(5, 5)
circuit3 = QuantumCircuit(1, 1)
circuit1.h(0)
circuit2.u2(0, math.pi, 0)
circuit3.u3(math.pi/2, math.pi, 0, 0)
circuit1.measure(0, 0)
circuit2.measure(0, 0)
circuit3.measure(0, 0)
print("Available QuaC backends:")
print(Quac.backends())
simulator = Quac.get_backend('fake_vigo_density_simulator', meas=True)
# Execute the circuit on the QuaC simulator
job1 = execute(circuit1, simulator)
job2 = execute(circuit2, simulator)
job3 = execute(circuit3, simulator)
print(f"Hadamard counts: {job1.result().get_counts()}")
print(f"U2 counts: {job2.result().get_counts()}")
print(f"U3 counts: {job3.result().get_counts()}")
plot_histogram(job1.result().get_counts())
plot_histogram((job2.result().get_counts()))
plot_histogram(job3.result().get_counts())
plt.show()
def setUp(self):
# Set up QuaC simulators
self.quac_sim = Quac.get_backend("fake_yorktown_density_simulator",
t1=True,
t2=False,
meas=False,
zz=False)
def main():
# Get backends
IBMQ.load_account()
provider = IBMQ.get_provider(hub="ibm-q-ornl", group="anl", project="chm168")
backend = provider.get_backend("ibmq_burlington")
plugin_backend = Quac.get_backend("fake_burlington_density_simulator", t1=True, t2=True, meas=False, zz=False)
# Get calibration circuits
hardware_properties = FakeBurlington().properties()
num_gates = np.linspace(10, 500, 30, dtype='int')
qubits = list(range(len(backend.properties().qubits)))
t1_circs, t1_delay = t1_circuits(num_gates,
hardware_properties.gate_length('id', [0]) * 1e9,
qubits)
t2_circs, t2_delay = t2_circuits((num_gates / 2).astype('int'),
hardware_properties.gate_length('id', [0]) * 1e9,
qubits)
# Formulate real noise model
real_noise_model = QuacNoiseModel(
t1_times=[1234, 2431, 2323, 2222, 3454],
t2_times=[12000, 14000, 14353, 20323, 30232]
)
# Formulate initial guess noise model (only same order of magnitude)
guess_noise_model = QuacNoiseModel(
t1_times=[1000, 1000, 1000, 1000, 1000],
t2_times=[10000, 10000, 10000, 10000, 10000]
)
# Calculate calibration circuit reference results
reference_job = execute(t1_circs + t2_circs, plugin_backend,
quac_noise_model=real_noise_model,
optimization_level=0)
reference_result = reference_job.result()
# Calculate optimized noise model
new_noise_model = optimize_noise_model_ng(
guess_noise_model=guess_noise_model,
circuits=t1_circs + t2_circs,
backend=plugin_backend,
reference_result=reference_result,
loss_function=angle_objective_function
)
# Show original noise model and optimized noise model
print(f"Original noise model: {real_noise_model}")
print(f"New noise model: {new_noise_model}")
def setUp(self):
# Set up QuaC simulator and QASM simulator
self.quac_simulator = Quac.get_backend(
"fake_yorktown_density_simulator")
self.qasm_simulator = Aer.get_backend("statevector_simulator")
"""
from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister, execute
from qiskit.visualization import plot_histogram
from quac_qiskit import Quac
from quac_qiskit.format import quac_time_qasm_transpiler
import matplotlib.pyplot as plt
if __name__ == '__main__':
# Note: measurement errors are included by setting the "meas" option to True
# Qubit P(0 | 1) P(1 | 0)
# 0 0.10799999999999998 0.024
# 1 0.039000000000000035 0.004
# 2 0.026 0.009000000000000008
# 3 0.03400000000000003 0.005
# 4 0.135 0.019
print(Quac.backends())
simulator = Quac.get_backend('fake_bogota_counts_simulator',
t1=False,
t2=False,
meas=True,
zz=True)
# Build the GhZ circuit over five qubits
quantum_register = QuantumRegister(4, "qreg")
classical_register1 = ClassicalRegister(2, "creg1")
classical_register2 = ClassicalRegister(2, "creg2")
ghz_circ = QuantumCircuit()
ghz_circ.add_register(quantum_register)
ghz_circ.add_register(classical_register1)
ghz_circ.add_register(classical_register2)