# SamplingVQE¶

class SamplingVQE(sampler, ansatz, optimizer, *, initial_point=None, aggregation=None, callback=None)[source]

Bases : VariationalAlgorithm, SamplingMinimumEigensolver

The Variational Quantum Eigensolver algorithm, optimized for diagonal Hamiltonians.

VQE is a hybrid quantum-classical algorithm that uses a variational technique to find the minimum eigenvalue of a given diagonal Hamiltonian operator $$H_{\text{diag}}$$.

In contrast to the VQE class, the SamplingVQE algorithm is executed using a sampler primitive.

An instance of SamplingVQE also requires an ansatz, a parameterized QuantumCircuit, to prepare the trial state $$|\psi(\vec\theta)\rangle$$. It also needs a classical optimizer which varies the circuit parameters $$\vec\theta$$ to minimize the objective function, which depends on the chosen aggregation.

The optimizer can either be one of Qiskit’s optimizers, such as SPSA or a callable with the following signature:

from qiskit.algorithms.optimizers import OptimizerResult

def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
# Note that the callable *must* have these argument names!
# Args:
#     fun (callable): the function to minimize
#     x0 (np.ndarray): the initial point for the optimization
#     jac (callable, optional): the gradient of the objective function
#     bounds (list, optional): a list of tuples specifying the parameter bounds

result = OptimizerResult()
result.x = # optimal parameters
result.fun = # optimal function value
return result


The above signature also allows one to use any SciPy minimizer, for instance as

from functools import partial
from scipy.optimize import minimize

optimizer = partial(minimize, method="L-BFGS-B")


The following attributes can be set via the initializer but can also be read and updated once the SamplingVQE object has been constructed.

sampler

The sampler primitive to sample the circuits.

Type

BaseSampler

ansatz

A parameterized quantum circuit to prepare the trial state.

Type

QuantumCircuit

optimizer

A classical optimizer to find the minimum energy. This can either be a Qiskit Optimizer or a callable implementing the Minimizer protocol.

Type
aggregation

A float or callable to specify how the objective function evaluated on the basis states should be aggregated. If a float, this specifies the $$\alpha \in [0,1]$$ parameter for a CVaR expectation value [1]. If a callable, it takes a list of basis state measurements specified as [(probability, objective_value)] and return an objective value as float. If None, all an ordinary expectation value is calculated.

Type

float | Callable[[list[tuple[float, complex]], float] | None

callback

A callback that can access the intermediate data at each optimization step. These data are: the evaluation count, the optimizer parameters for the ansatz, the evaluated value, and the metadata dictionary.

Type

Callable[[int, np.ndarray, float, dict[str, Any]], None] | None

Références

[1]: Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., and Woerner, S.,

« Improving Variational Quantum Optimization using CVaR » arXiv:1907.04769

Paramètres
• sampler (BaseSampler) – The sampler primitive to sample the circuits.

• ansatz (QuantumCircuit) – A parameterized quantum circuit to prepare the trial state.

• optimizer (Optimizer | Minimizer) – A classical optimizer to find the minimum energy. This can either be a Qiskit Optimizer or a callable implementing the Minimizer protocol.

• initial_point (Sequence[float] | None) – An optional initial point (i.e. initial parameter values) for the optimizer. The length of the initial point must match the number of ansatz parameters. If None, a random point will be generated within certain parameter bounds. SamplingVQE will look to the ansatz for these bounds. If the ansatz does not specify bounds, bounds of $$-2\pi$$, $$2\pi$$ will be used.

• aggregation (float | Callable[[list[float]], float] | None) – A float or callable to specify how the objective function evaluated on the basis states should be aggregated.

• callback (Callable[[int, np.ndarray, float, dict[str, Any]], None] | None) – A callback that can access the intermediate data at each optimization step. These data are: the evaluation count, the optimizer parameters for the ansatz, the estimated value, and the metadata dictionary.

Methods

 compute_minimum_eigenvalue Compute the minimum eigenvalue of a diagonal operator. supports_aux_operators Whether computing the expectation value of auxiliary operators is supported.

Attributes

initial_point

Return the initial point.