VariationalPrinciple

class qiskit.algorithms.time_evolvers.variational.VariationalPrinciple(qgt, gradient)

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

Parameter:

• 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)

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

Parameter:

• 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.

Rückgabe:

An evolution gradient.

Rückgabetyp:

np.ndarray

metric_tensor(ansatz, param_values)

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

Parameter:

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

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

Rückgabe:

Metric tensor.

Verursacht:

AlgorithmError – If a QFI job fails.

Rückgabetyp:

Sequence[float]