ReverseQGT#

class ReverseQGT(phase_fix=True, derivative_type=DerivativeType.COMPLEX)[source]#

Bases: BaseQGT

QGT calculation with the classically efficient reverse mode.

Note

This QGT implementation is based on statevector manipulations and scales exponentially with the number of qubits. However, for small system sizes it can be very fast compared to circuit-based gradients.

This class implements the calculation of the QGT as described in [1]. By keeping track of three statevectors and iteratively sweeping through each parameterized gate, this method scales only quadratically with the number of parameters.

References:

[1]: Jones, T. “Efficient classical calculation of the Quantum Natural Gradient” (2020).

arXiv:2011.02991.

Parameters:

  • phase_fix (bool) – Whether or not to include the phase fix.

  • derivative_type (DerivativeType) – Determines whether the complex QGT or only the real or imaginary parts are calculated.

Attributes

SUPPORTED_GATES = ['rx', 'ry', 'rz', 'cp', 'crx', 'cry', 'crz']#
derivative_type#

The derivative type.

options#

There are no options for the reverse QGT, returns an empty options dict.

Returns:

Empty options.

Methods

run(circuits, parameter_values, parameters=None, **options)#

Run the job of the QGTs on the given circuits.

Parameters:

  • circuits (Sequence[QuantumCircuit]) – The list of quantum circuits to compute the QGTs.

  • parameter_values (Sequence[Sequence[float]]) – The list of parameter values to be bound to the circuit.

  • parameters (Sequence[Sequence[Parameter] | None] | None) – The sequence of parameters to calculate only the QGTs of the specified parameters. Each sequence of parameters corresponds to a circuit in circuits. Defaults to None, which means that the QGTs of all parameters in each circuit are calculated.

  • options – Primitive backend runtime options used for circuit execution. The order of priority is: options in run method > QGT’s default options > primitive’s default setting. Higher priority setting overrides lower priority setting.

Returns:

The job object of the QGTs of the expectation values. The i-th result corresponds to circuits[i] evaluated with parameters bound as parameter_values[i].

Raises:

ValueError – Invalid arguments are given.

Return type:

AlgorithmJob

update_default_options(**options)#

Update the gradient’s default options setting.

Parameters:

**options – The fields to update the default options.