VariationalPrinciple#

class VariationalPrinciple(qgt, gradient)[source]#

Bases: ABC

A Variational Principle class. It determines the time propagation of parameters in a quantum state provided as a parametrized quantum circuit (ansatz).

qgt#

Instance of a class used to compute the GQT.

Type:

BaseQGT

gradient#

Instance of a class used to compute the state gradient.

Type:

BaseEstimatorGradient

Parameters:

  • qgt (BaseQGT) – Instance of a class used to compute the GQT.

  • gradient (BaseEstimatorGradient) – Instance of a class used to compute the state gradient.

Methods

abstract evolution_gradient(hamiltonian, ansatz, param_values, gradient_params=None)[source]#

Calculates an evolution gradient according to the rules of this variational principle.

Parameters:

  • hamiltonian (BaseOperator) – Operator used for Variational Quantum Time Evolution.

  • ansatz (QuantumCircuit) – Quantum state in the form of a parametrized quantum circuit.

  • param_values (Sequence[float]) – Values of parameters to be bound.

  • gradient_params (Sequence[Parameter] | None) – List of parameters with respect to which gradients should be computed. If None given, gradients w.r.t. all parameters will be computed.

Returns:

An evolution gradient.

Return type:

np.ndarray

metric_tensor(ansatz, param_values)[source]#

Calculates a metric tensor according to the rules of this variational principle.

Parameters:

  • ansatz (QuantumCircuit) – Quantum state in the form of a parametrized quantum circuit.

  • param_values (Sequence[float]) – Values of parameters to be bound.

Returns:

Metric tensor.

Raises:

AlgorithmError – If a QFI job fails.

Return type:

Sequence[float]