Backends

Choose a backend to run circuits over Forge using statevector simulation or measurement-based computing. Refer to these Jupyter Notebook tutorials on Forge:

  • GettingStarted/Examples/Circuits/start_here.ipynb

  • GettingStarted/Examples/Circuits/quasar_measurement.ipynb

Classes

Class Backend

Class QuasarSimulatorBackend

Class QiskitHardwareBackend

  • has_run_statevector()

  • has_statevector_input()

  • run_measurement()

  • run_statevector()

  • summary_str()

Class QiskitSimulatorBackend

  • has_run_statevector()

  • has_statevector_input()

  • run_measurement()

  • run_statevector()

  • summary_str()

Class CirqSimulatorBackend

  • has_run_statevector()

  • has_statevector_input()

  • run_measurement()

  • run_statevector()

  • summary_str()

Module Measurement

Backend

class quasar.backend.Backend

Class Backend represents a physical or simulated quantum circuit resource. Backends must implement run_measurement, from which many higher-order quantities may be computed, e.g., run_pauli_expectation, run_pauli_expectation_value, run_pauli_expectation_value_gradient, etc. Backends supporting classical statevector-based simulation may also optionally implement run_statevector, from which many additional higher-order quantities may be computed, e.g., run_unitary, run_density_matrix, and ideal-infinite-sampled versions of the previously-discussed higher-order quantities. Backends may additionally overload any of the stock higher-order methods declared here, potentially providing increased performance or additional methodology.

property has_run_statevector

Does this Backend support run_statevector?

Backend subclasses should OVERLOAD this method.

Returns

True if run_statevector is supported else False.

Return type

bool

property has_statevector_input

Does this Backend allow statevector to be passed as input argument to various run methods?

Backend subclasses should OVERLOAD this method.

Returns

True if statevector input arguments can be supplied else False.

Return type

bool

run_measurement(circuit, nmeasurement=None, statevector=None, min_qubit=None, nqubit=None, dtype=<class 'numpy.complex128'>, **kwargs)

Run a circuit using measurement-based computing.

Parameters

  • circuit (Circuit) – the circuit to run

  • nmeasurement (int or None) – number of measurements to sample. If None, infinite sampling is assumed and the probabilities are directly returned in ProbabilityHistogram format (default - None)

  • statevector (np.ndarray, shape 2**N) – input statevector (default - None)

  • min_qubit (int) – the minimum occupied qubit index (default - None)

  • nqubit (int) – number of qubits (default - None)

  • dtype (real or complex dtype) – the dtype to perform the computation at. The gate operator will be cast to this dtype. Note that using real dtypes (float64 or float32) can reduce storage and runtime, but the imaginary parts of the input wfn and all gate unitary operators will be discarded without checking. In these cases, the user is responsible for ensuring that the circuit works on O(2^N) rather than U(2^N) and that the output is valid. (default - np.complex128)

Returns

histogram of projective measurements taken while running the circuit

Return type

ProbabilityHistogram

run_measurements(circuits, nmeasurement=None)

Run a sequence of circuits using measurement-based computing.

Returns a sequence of ProbabilityHistogram objects.

As the circuits could be very different, statevector input or min_qubit/nqubit are not supported. The default behaviour is to simply run the circuits in sequence, but on some hardware backends a speed advantage can be gained by batching circuits into jobs.

Parameters

  • circuits (Sequence[Circuit]) – the circuits to run

  • nmeasurement (int or Sequence[int] or None) – number of measurements to sample. If None, infinite sampling is assumed and the probabilities are directly returned in ProbabilityHistogram format (default - None) If an int, that number of measurements will be used for all circuits. If a sequence, run_measurements returns the shortest of len(circuits) and len(nmeasurement).

Returns

list of histograms of projective measurements taken while running the circuits

Return type

list[ProbabilityHistogram]

run_pauli_expectation_value(circuit, pauli, nmeasurement=None, statevector=None, min_qubit=None, nqubit=None, dtype=<class 'numpy.complex128'>, **kwargs)

Compute the total observable expectation value of a Pauli-sparse Hermitian operator using the statevector generated by the circuit at the current parameter set.

Parameters

  • circuit (Circuit) – the circuit for which to calculate the expectation value.

  • pauli (Pauli) – the Hermitian operator supplied in sparse Pauli form.

  • nmeasurement (int) – number of measurements to sample. If default value None, assume infinite sampling.

  • statevector (np.ndarray of shape 2**N, complex dtype) – the statevector generated by the circuit. If default value None, all qubits in the statevector set equal to 0.

  • min_qubit (int) – the minimum occupied qubit index (default - None).

  • nqubit (int) – the total number of qubit indices in the circuit, including empty qubit indices (default - None).

  • dtype (complex dtype) – the dtype to perform the computation at. (default - np.complex128).

Returns

total observable expectation value

Return type

float, complex dtype

run_pauli_expectation_value_gradient(circuit, pauli, nmeasurement=None, statevector=None, min_qubit=None, nqubit=None, dtype=<class 'numpy.complex128'>, parameter_indices=None, **kwargs)

Compute the gradient, with respect to a circuit’s current parameter set, of the total observable expectation value of a Pauli-sparse Hermitianoperatorusing the statevector generated by the circuit at the current parameter set.

Parameters

  • circuit (Circuit) – the circuit for which to calculate the expectation value.

  • pauli (Pauli) – the Hermitian operator supplied in sparse Pauli form.

  • nmeasurement (int) – number of measurements to sample. If default value None, assume infinite sampling.

  • statevector (np.ndarray of shape 2**N, complex dtype) – the statevector generated by the circuit. If default value None, all qubits in the statevector set equal to 0.

  • min_qubit (int) – the minimum occupied qubit index (default - None).

  • nqubit (int) – the total number of qubit indices in the circuit, including empty qubit indices (default - None).

  • dtype (complex dtype) – the dtype to perform the computation at. (default - np.complex128).

  • parameter_indices (list of ints) – take the gradient with respect to these parameter indices. If default value None, take the gradient with respect to all parameters.

Returns

np.ndarray with shape equal to parameter_indices, complex dtype: gradient of total observable expectation value with respect to the circuit’s current parameter set.

run_statevector(circuit, statevector=None, min_qubit=None, nqubit=None, dtype=<class 'numpy.complex128'>, **kwargs)

Run a circuit using statevector simulation.

Backend subclasses should OVERLOAD this method.

Parameters

  • circuit (Circuit) – the circuit to run

  • statevector (np.ndarray, shape 2**N) – input statevector (default - None)

  • min_qubit (int) – the minimum occupied qubit index (default - None)

  • nqubit (int) – number of qubits (default - None)

  • dtype (real or complex dtype) – the dtype to perform the computation at. The gate operator will be cast to this dtype. Note that using real dtypes (float64 or float32) can reduce storage and runtime, but the imaginary parts of the input wfn and all gate unitary operators will be discarded without checking. In these cases, the user is responsible for ensuring that the circuit works on O(2^N) rather than U(2^N) and that the output is valid. (default - np.complex128)

Returns

full output statevector

Return type

np.ndarray, shape 2**N

property summary_str

A more-extensive string representation of this Backend, optionally including current hardware state.

Backend subclasses should OVERLOAD this method.

Returns

multiline string representation of this Backend

Return type

str

QuasarBackend

class quasar.quasar_backend.QuasarSimulatorBackend
property has_run_statevector

Does this Backend support run_statevector?

Backend subclasses should OVERLOAD this method.

Returns

True if run_statevector is supported else False.

Return type

bool

property has_statevector_input

Does this Backend allow statevector to be passed as input argument to various run methods?

Backend subclasses should OVERLOAD this method.

Returns

True if statevector input arguments can be supplied else False.

Return type

bool

run_statevector(circuit, statevector=None, min_qubit=None, nqubit=None, dtype=<class 'numpy.complex128'>)

Run a circuit using statevector simulation.

Backend subclasses should OVERLOAD this method.

Parameters

  • circuit (Circuit) – the circuit to run

  • statevector (np.ndarray, shape 2**N) – input statevector (default - None)

  • min_qubit (int) – the minimum occupied qubit index (default - None)

  • nqubit (int) – number of qubits (default - None)

  • dtype (real or complex dtype) – the dtype to perform the computation at. The gate operator will be cast to this dtype. Note that using real dtypes (float64 or float32) can reduce storage and runtime, but the imaginary parts of the input wfn and all gate unitary operators will be discarded without checking. In these cases, the user is responsible for ensuring that the circuit works on O(2^N) rather than U(2^N) and that the output is valid. (default - np.complex128)

Returns

full output statevector

Return type

np.ndarray, shape 2**N

property summary_str

A more-extensive string representation of this Backend, optionally including current hardware state.

Backend subclasses should OVERLOAD this method.

Returns

multiline string representation of this Backend

Return type

str

QiskitBackend

CirqBackend

Measurement

class quasar.measurement.CountHistogram(nqubit, histogram={}, nmeasurement=None)

Class CountHistogram represents the output of thhe measurement process for a Circuit. A CountHistogram object contains the number of qubits measured (nqubit attribute), the total number of measurements made (nmeasurement attribute), and a histogram of binary strings representing the collective measurement result (Ket strings) together with the integral number of times that each one was observed.

To access the probability corresponding to a specific Ket string, index into the CountHistogram instance with the integer equivalent of the binary Ket string or the binary string itself.

to_probability_histogram()

Convert from a CountHistogram to a ProbabilityHistogram.

class quasar.measurement.ProbabilityHistogram(nqubit, histogram={}, nmeasurement=None)

Class ProbabilityHistogram represents the output of the measurement process for a Circuit. A ProbabilityHistogram object contains the number of qubits measured (nqubit attribute), the total number of measurements made (nmeasurement attribute), and a histogram of binary strings representing each collective measurement result (Ket strings) together with the probability that each one was observed.

To access the probability corresponding to a specific Ket string, index into the ProbabilityHistogram instance with the integer equivalent of the binary Ket string or the binary string itself.

to_count_histogram()

Convert from a ProbabilityHistogram to a CountHistogram.