AdaptVQE
AdaptVQE(solver, *, threshold=1e-05, max_iterations=None)
Bases: qiskit.algorithms.variational_algorithm.VariationalAlgorithm, qiskit.algorithms.minimum_eigensolvers.minimum_eigensolver.MinimumEigensolver
The Adaptive Variational Quantum Eigensolver algorithm.
AdaptVQE(opens in a new tab) is a quantum algorithm which creates a compact ansatz from a set of evolution operators. It iteratively extends the ansatz circuit, by selecting the building block that leads to the largest gradient from a set of candidates. In chemistry, this is usually a list of orbital excitations. Thus, a common choice of ansatz to be used with this algorithm is the Unitary Coupled Cluster ansatz implemented in Qiskit Nature. This results in a wavefunction ansatz which is uniquely adapted to the operator whose minimum eigenvalue is being determined. This class relies on a supplied instance of VQE to find the minimum eigenvalue. The performance of AdaptVQE significantly depends on the minimization routine.
from qiskit.algorithms.minimum_eigensolvers import AdaptVQE, VQE
from qiskit.algorithms.optimizers import SLSQP
from qiskit.primitives import Estimator
from qiskit.circuit.library import EvolvedOperatorAnsatz
# get your Hamiltonian
hamiltonian = ...
# construct your ansatz
ansatz = EvolvedOperatorAnsatz(...)
vqe = VQE(Estimator(), ansatz, SLSQP())
adapt_vqe = AdaptVQE(vqe)
eigenvalue, _ = adapt_vqe.compute_minimum_eigenvalue(hamiltonian)The following attributes can be set via the initializer but can also be read and updated once the AdaptVQE object has been constructed.
solver
a VQE instance used internally to compute the minimum eigenvalues. It is a requirement that the ansatz of this solver is of type qiskit.circuit.library.EvolvedOperatorAnsatz.
threshold
the convergence threshold for the algorithm. Once all gradients have an absolute value smaller than this threshold, the algorithm terminates.
max_iterations
the maximum number of iterations for the adaptive loop. If None, the algorithm is not bound in its number of iterations.
Parameters
- solver (VQE) – a
VQEinstance used internally to compute the minimum eigenvalues. It is a requirement that theansatzof this solver is of typeqiskit.circuit.library.EvolvedOperatorAnsatz. - threshold (float) – the convergence threshold for the algorithm. Once all gradients have an absolute value smaller than this threshold, the algorithm terminates.
- max_iterations (int | None) – the maximum number of iterations for the adaptive loop. If
None, the algorithm is not bound in its number of iterations.
Methods
compute_minimum_eigenvalue
AdaptVQE.compute_minimum_eigenvalue(operator, aux_operators=None)
Computes the minimum eigenvalue.
Parameters
- operator (BaseOperator | PauliSumOp) – Operator whose minimum eigenvalue we want to find.
- aux_operators (ListOrDict[BaseOperator | PauliSumOp] | None) – Additional auxiliary operators to evaluate.
Raises
- TypeError – If an ansatz other than
EvolvedOperatorAnsatzis provided. - QiskitError – If all evaluated gradients lie below the convergence threshold in the first iteration of the algorithm.
Return type
Returns
An AdaptVQEResult which is a VQEResult but also but also includes runtime information about the AdaptVQE algorithm like the number of iterations, termination criterion, and the final maximum gradient.
supports_aux_operators
classmethod AdaptVQE.supports_aux_operators()
Whether computing the expectation value of auxiliary operators is supported.
If the minimum eigensolver computes an eigenvalue of the main operator then it can compute the expectation value of the aux_operators for that state. Otherwise they will be ignored.
Return type
bool
Returns
True if aux_operator expectations can be evaluated, False otherwise