VQE

class VQE(hamiltonian=None, molecule=None, n_electrons=None, n_layers=1, ansatz=None, initial_state=None, max_iterations=10, **kwargs)[source]

Bases: VariationalQuantumAlgorithm

Variational Quantum Eigensolver (VQE) implementation.

VQE is a hybrid quantum-classical algorithm used to find the ground state energy of a given Hamiltonian. It works by preparing a parameterized quantum state (ansatz) and optimizing the parameters to minimize the expectation value of the Hamiltonian.

The algorithm can work with either: - A molecular Hamiltonian (for quantum chemistry problems) - A custom Hamiltonian operator

Variables:

  • ansatz (Ansatz) – The parameterized quantum circuit ansatz.

  • n_layers (int) – Number of ansatz layers.

  • n_qubits (int) – Number of qubits in the system.

  • n_electrons (int) – Number of electrons (for molecular systems).

  • cost_hamiltonian – The Hamiltonian to minimize.

  • loss_constant (float) – Constant term extracted from the Hamiltonian.

  • molecule – The molecule object (if applicable).

  • optimizer – Classical optimizer for parameter updates.

  • max_iterations (int) – Maximum number of optimization iterations.

  • current_iteration (int) – Current optimization iteration.

Initialize the VQE problem.

Parameters:

  • hamiltonian (Operator | SparsePauliOp | None) – A Hamiltonian representing the problem. Defaults to None.

  • molecule (Molecule | None) – The molecule representing the problem. Defaults to None.

  • n_electrons (int | None) – Number of electrons associated with the Hamiltonian. Only needed when a Hamiltonian is given. Defaults to None.

  • n_layers (int) – Number of ansatz layers. Defaults to 1.

  • ansatz (Ansatz | None) – The ansatz to use for the VQE problem. Defaults to HartreeFockAnsatz.

  • initial_state (InitialState | None) – Initial state preparation. Pass an InitialState instance (e.g. ZerosState(), SuperpositionState()). Defaults to ZerosState() if None.

  • max_iterations (int) – Maximum number of optimization iterations. Defaults to 10.

  • **kwargs – Additional keyword arguments passed to the parent class.

Attributes Summary

eigenstate

Get the computed eigenstate as a NumPy array.

n_params_per_layer

Number of trainable parameters per ansatz layer.

Methods Summary

sample_solution([params])

Extract the eigenstate corresponding to the lowest energy found.

Attributes Documentation

eigenstate

Get the computed eigenstate as a NumPy array.

Returns:

The array of bits of the lowest energy eigenstate,

or None if not computed.

Return type:

npt.NDArray[np.int32] | None

n_params_per_layer

Number of trainable parameters per ansatz layer.

Returns:

Parameters per layer for the current ansatz, qubit count, and electron count.

Return type:

int

Methods Documentation

sample_solution(params=None, **kwargs)[source]

Extract the eigenstate corresponding to the lowest energy found.

Return type:

VQE