def main(backend_specs):
# Define registers and circuit
q = QuantumRegister(2)
c = ClassicalRegister(2)
circuit = QuantumCircuit(q, c)
# Quantum circuit starts here
circuit.h(q[0])
circuit.cnot(q[0], q[1])
# Note we have to remove the Measurement to convert to Zapata's circuit format
# End quantum circuit
# After this point we use the power of Orquestra to use different backends!
# Use Zapata's representation of quantum circuits
zap_circuit = Circuit(circuit)
# Build a backend from the specs we passed to the step
if isinstance(backend_specs, str):
backend_specs_dict = yaml.load(backend_specs, Loader=yaml.SafeLoader)
else:
backend_specs_dict = backend_specs
backend = create_object(backend_specs_dict)
# We can use this backend to get the bitstring distribution
distribution = backend.get_bitstring_distribution(zap_circuit)
# Finally, we can save the output!
save_bitstring_distribution(distribution, "output-distribution.json")
def get_bars_and_stripes_target_distribution(nrows,
ncols,
fraction=1.,
method="zigzag"):
''' Generates bars and stripes (BAS) data in zigzag pattern
Args:
nrows (int): number of rows in BAS dataset
ncols (int): number of columns in BAS dataset
fraction (float): maximum fraction of patterns to include (at least one pattern will always be included)
method (string): the method to use to label the qubits
Returns:
Array of list of BAS pattern.
'''
if method == "zigzag":
data = bars_and_stripes_zigzag(nrows, ncols)
else:
raise RuntimeError(
"Method: {} is not supported for generated a target distribution for bars and stripes"
.format(method))
# Remove patterns until left with a subset that has cardinality less than or equal to the percentage * total number of patterns
num_desired_patterns = int(len(data) * fraction)
num_desired_patterns = max(num_desired_patterns, 1)
data = random.sample(list(data), num_desired_patterns)
distribution_dict = {}
for pattern in data:
bitstring = ""
for qubit in pattern:
bitstring += str(qubit)
distribution_dict[bitstring] = 1.
save_bitstring_distribution(BitstringDistribution(distribution_dict),
"distribution.json")
def get_bitstring_distribution(
backend_specs: Dict,
circuit: str,
noise_model: str = "None",
device_connectivity: str = "None",
):
backend_specs = json.loads(backend_specs)
if noise_model != "None":
backend_specs["noise_model"] = load_noise_model(noise_model)
if device_connectivity != "None":
backend_specs["device_connectivity"] = load_circuit_connectivity(
device_connectivity)
backend = create_object(backend_specs)
circuit = load_circuit(circuit)
bitstring_distribution = backend.get_bitstring_distribution(circuit)
save_bitstring_distribution(bitstring_distribution,
"bitstring-distribution.json")
def get_bitstring_distribution(
backend_specs: Specs,
circuit: str,
noise_model: Optional[str] = None,
device_connectivity: Optional[str] = None,
):
if isinstance(backend_specs, str):
backend_specs = json.loads(backend_specs)
if noise_model is not None:
backend_specs["noise_model"] = load_noise_model(noise_model)
if device_connectivity is not None:
backend_specs["device_connectivity"] = layouts.load_circuit_connectivity(
device_connectivity
)
backend = create_object(backend_specs)
circuit = circuits.load_circuit(circuit)
bitstring_distribution = backend.get_bitstring_distribution(circuit)
save_bitstring_distribution(bitstring_distribution, "bitstring-distribution.json")
def get_bars_and_stripes_target_distribution(nrows, ncols, fraction, method):
distribution = _get_bars_and_stripes_target_distribution(
nrows, ncols, fraction, method)
save_bitstring_distribution(distribution, "distribution.json")
