def __init__(self, api: QuantumInspireAPI, provider: Any,
configuration: Optional[QasmBackendConfiguration] = None) -> None:
""" Python implementation of a quantum simulator using Quantum Inspire API.
Args:
api: The interface instance to the Quantum Inspire API.
provider: Provider for this backend.
configuration: The configuration of the quantum inspire backend. The
configuration must implement the fields given by the QiSimulatorPy.DEFAULT_CONFIGURATION. All
configuration fields are listed in the table below. The table rows with an asterisk specify fields which
can have a custom value and are allowed to be changed according to the description column.
| key | description
|-------------------------|----------------------------------------------------------------------------
| backend_name (str)* | The name of the quantum inspire backend. The API can list the name of each
| | available backend using the function api.list_backend_types(). One of the
| | listed names must be used.
| backend_version (str) | Backend version in the form X.Y.Z.
| n_qubits (int) | Number of qubits.
| basis_gates (list[str]) | A list of basis gates to compile to.
| gates (GateConfig) | List of basis gates on the backend. Not used.
| local (bool) | Indicates whether the system is running locally or remotely. Not used.
| simulator (bool) | Specifies whether the backend is a simulator or a quantum system. Not used.
| conditional (bool) | Backend supports conditional operations.
| open_pulse (bool) | Backend supports open pulse. False.
| memory (bool) | Backend supports memory. True.
| max_shots (int) | Maximum number of shots supported.
| max_experiments (int) | Optional: Maximum number of experiments (circuits) per job.
"""
super().__init__(configuration=(configuration or
QuantumInspireBackend.DEFAULT_CONFIGURATION),
provider=provider)
self.__backend: Dict[str, Any] = api.get_backend_type_by_name(self.name())
self.__api: QuantumInspireAPI = api
def __init__(self, api: QuantumInspireAPI, provider: Any,
configuration: Optional[QasmBackendConfiguration] = None) -> None:
""" Python implementation of a quantum simulator using Quantum Inspire API.
:param api: The interface instance to the Quantum Inspire API.
:param provider: Provider for this backend.
:param configuration: The configuration of the Quantum Inspire backend. The
configuration must implement the fields given by the QiSimulatorPy.DEFAULT_CONFIGURATION. All
configuration fields are listed in the table below. The table rows with an asterisk specify fields which
can have a custom value and are allowed to be changed according to the description column.
=================== ============= =====================================================================
Key Type Description
=================== ============= =====================================================================
``backend_name`` * str The name of the Quantum Inspire backend. The API can list the name of
each available backend using the function api.list_backend_types().
One of the listed names must be used.
``backend_version`` str Backend version in the form X.Y.Z.
``n_qubits`` int Number of qubits.
``basis_gates`` list[str] A list of basis gates to compile to.
``gates`` GateConfig List of basis gates on the backend. Not used.
``local`` bool Indicates whether the system is running locally or remotely.
``simulator`` bool Specifies whether the backend is a simulator or a quantum system.
``conditional`` bool Backend supports conditional operations.
``open_pulse`` bool Backend supports open pulse. False.
``memory`` bool Backend supports memory. True.
``max_shots`` int Maximum number of shots supported.
``max_experiments`` int Optional: Maximum number of experiments (circuits) per job.
``coupling_map`` list[list] Define the edges.
=================== ============= =====================================================================
"""
super().__init__(configuration=(configuration or
QuantumInspireBackend.DEFAULT_CONFIGURATION),
provider=provider)
self.__backend: Dict[str, Any] = api.get_backend_type_by_name(self.name())
self.__api: QuantumInspireAPI = api
qubits,
unitary,
extra_empty_bits=extra_empty_bits,
custom_prepare=custom_prepare)
final_qasm = optimize(final_qasm, nancillas, qubits, extra_empty_bits)
if topology is not None:
final_qasm = map_to_topology(topology, final_qasm)
final_qasm = optimize(final_qasm, nancillas, qubits, extra_empty_bits)
if error_toggle:
final_qasm = introduce_error(final_qasm, mu, sigma)
final_qasm = optimize(final_qasm, nancillas, qubits, extra_empty_bits)
backend_type = qi.get_backend_type_by_name('QX single-node simulator')
result = qi.execute_qasm(final_qasm,
backend_type=backend_type,
number_of_shots=shots)
# Classical postprocessing
fraction, error = print_result(
remove_degeneracy(result['histogram'], nancillas),
desired_bit_accuracy, nancillas)
print('Fraction: ', fraction)
print('Error: ', error)
print('Correct chance: ', 1 - (1 - p_succes)**shots)
def testErrorCorrection():
server_url = r'https://api.quantum-inspire.com'
print('Enter mail address')
email = input()
print('Enter password')
password = getpass()
auth = (email, password)
qi = QuantumInspireAPI(server_url, auth)
backend = qi.get_backend_type_by_name('QX single-node simulator')
inputValues = [
{
'theta': 0,
'phi': 0
}, #|0>
{
'theta': np.pi,
'phi': np.pi
}, #|1>
{
'theta': np.pi / 2,
'phi': np.pi / 2
} #|+>
]
for inputValue in inputValues:
print("Using input value " + str(inputValue))
allErrorIdxs = []
for nrOfErrors in range(2):
allErrorIdxs += itertools.combinations(range(nbqubits), nrOfErrors)
# iterate over all possible combinations of errors
for errorIdxs in allErrorIdxs:
inputStateComplex = _stateForInput(inputValue['theta'],
inputValue['phi'])
print("Introducing errors, indices " + str(errorIdxs))
QASM = createProgram(inputValue, errorIdxs)
prob_0 = 0
prob_1 = 0
if 1: # Wavefunction
result = qi.execute_qasm(QASM,
backend_type=backend,
number_of_shots=1)
else: # Do measurements
result = qi.execute_qasm(QASM,
backend_type=backend,
number_of_shots=10)
result = result['histogram']
for key, value in zip(result.keys(), result.values()):
if (int(key) % 2) == 0: #Number is odd
prob_0 += value
else:
prob_1 += value
if abs(prob_0 - abs(inputStateComplex[0])**2) < 0.0001 and abs(
prob_1 - abs(inputStateComplex[1])**2) < 0.0001:
print("Error correction works")
else:
print("Error correction fails")