paraqeet.measurement.unitary_fidelity.UnitaryFidelity#

class paraqeet.measurement.unitary_fidelity.UnitaryFidelity(propagation, gate, basis_states=None)[source]#

Bases: NormalizableMeasurement, Differentiable

Unitary fidelity measurement model.

Fidelity measure that compares the propagator with a desired gate by way of L2 norm.

Parameters:

  • propagation (Propagation) – Implementation of EOM solver.

  • gate (Array) – Matrix representation of target gate.

  • times (Array) – List of times to compare. Should have length 2. More is allowed, but only the first and last are used.

  • basis_states (Array optional) – List of basis states. If set the ideal and actual gate are applied to these states and their pairwise overlap computed, equivalent to the L2 trace norm. Defaults to [].

__init__(propagation, gate, basis_states=None)[source]#
Parameters:

Methods

__init__(propagation, gate[, basis_states])

calculate_normalized_scalar(times)

Return the L2 norm of the last time step compared to the ideal gate.

get_value_and_gradient(times)

Get the L2 norm and the analytic expression for the gradient.

measure(times)

Return measurement in the range [0, 1].

set_ideal_gate(gate)

Compute target states for the L2 norm.
calculate_normalized_scalar(times)[source]#

Return the L2 norm of the last time step compared to the ideal gate.

Returns:

L2 norm of the last time step compared to the ideal gate.

Return type:

Array

Parameters:

times (Array | float)

get_value_and_gradient(times)[source]#

Get the L2 norm and the analytic expression for the gradient.

Returns:

Tuple of function value and gradient of shape (n_parameters,).

Return type:

Tuple[Array Array]

Parameters:

times (Array)

measure(times)[source]#

Return measurement in the range [0, 1].

Parameters:

times (Array)

Return type:

Array | float

set_ideal_gate(gate)[source]#

Compute target states for the L2 norm.

Parameters:

gate (Array) – Target state computation via this gate.