163 lines
9.5 KiB
Plaintext
163 lines
9.5 KiB
Plaintext
---
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title: SamplingVQE
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description: API reference for qiskit.algorithms.minimum_eigensolvers.SamplingVQE
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in_page_toc_min_heading_level: 1
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python_api_type: class
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python_api_name: qiskit.algorithms.minimum_eigensolvers.SamplingVQE
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---
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# SamplingVQE
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<Class id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.23/qiskit/algorithms/minimum_eigensolvers/sampling_vqe.py" signature="SamplingVQE(sampler, ansatz, optimizer, *, initial_point=None, aggregation=None, callback=None)" modifiers="class">
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Bases: `qiskit.algorithms.variational_algorithm.VariationalAlgorithm`, [`qiskit.algorithms.minimum_eigensolvers.sampling_mes.SamplingMinimumEigensolver`](qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolver "qiskit.algorithms.minimum_eigensolvers.sampling_mes.SamplingMinimumEigensolver")
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The Variational Quantum Eigensolver algorithm, optimized for diagonal Hamiltonians.
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VQE is a hybrid quantum-classical algorithm that uses a variational technique to find the minimum eigenvalue of a given diagonal Hamiltonian operator $H_{\text{diag}}$.
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In contrast to the [`VQE`](qiskit.algorithms.minimum_eigensolvers.VQE "qiskit.algorithms.minimum_eigensolvers.VQE") class, the `SamplingVQE` algorithm is executed using a [`sampler`](#qiskit.algorithms.minimum_eigensolvers.SamplingVQE.sampler "qiskit.algorithms.minimum_eigensolvers.SamplingVQE.sampler") primitive.
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An instance of `SamplingVQE` also requires an [`ansatz`](#qiskit.algorithms.minimum_eigensolvers.SamplingVQE.ansatz "qiskit.algorithms.minimum_eigensolvers.SamplingVQE.ansatz"), a parameterized [`QuantumCircuit`](qiskit.circuit.QuantumCircuit "qiskit.circuit.QuantumCircuit"), to prepare the trial state $|\psi(\vec\theta)\rangle$. It also needs a classical [`optimizer`](#qiskit.algorithms.minimum_eigensolvers.SamplingVQE.optimizer "qiskit.algorithms.minimum_eigensolvers.SamplingVQE.optimizer") which varies the circuit parameters $\vec\theta$ to minimize the objective function, which depends on the chosen [`aggregation`](#qiskit.algorithms.minimum_eigensolvers.SamplingVQE.aggregation "qiskit.algorithms.minimum_eigensolvers.SamplingVQE.aggregation").
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The optimizer can either be one of Qiskit’s optimizers, such as [`SPSA`](qiskit.algorithms.optimizers.SPSA "qiskit.algorithms.optimizers.SPSA") or a callable with the following signature:
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```python
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from qiskit.algorithms.optimizers import OptimizerResult
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def my_minimizer(fun, x0, jac=None, bounds=None) -> OptimizerResult:
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# Note that the callable *must* have these argument names!
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# Args:
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# fun (callable): the function to minimize
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# x0 (np.ndarray): the initial point for the optimization
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# jac (callable, optional): the gradient of the objective function
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# bounds (list, optional): a list of tuples specifying the parameter bounds
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result = OptimizerResult()
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result.x = # optimal parameters
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result.fun = # optimal function value
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return result
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```
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The above signature also allows one to use any SciPy minimizer, for instance as
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```python
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from functools import partial
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from scipy.optimize import minimize
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optimizer = partial(minimize, method="L-BFGS-B")
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```
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The following attributes can be set via the initializer but can also be read and updated once the `SamplingVQE` object has been constructed.
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### sampler
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<Attribute id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.sampler">
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The sampler primitive to sample the circuits.
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**Type**
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[BaseSampler](qiskit.primitives.BaseSampler "qiskit.primitives.BaseSampler")
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</Attribute>
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### ansatz
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<Attribute id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.ansatz">
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A parameterized quantum circuit to prepare the trial state.
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**Type**
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[QuantumCircuit](qiskit.circuit.QuantumCircuit "qiskit.circuit.QuantumCircuit")
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</Attribute>
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### optimizer
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<Attribute id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.optimizer">
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A classical optimizer to find the minimum energy. This can either be a Qiskit [`Optimizer`](qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.Optimizer") or a callable implementing the [`Minimizer`](qiskit.algorithms.optimizers.Minimizer "qiskit.algorithms.optimizers.Minimizer") protocol.
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**Type**
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[Optimizer](qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.Optimizer") | [Minimizer](qiskit.algorithms.optimizers.Minimizer "qiskit.algorithms.optimizers.Minimizer")
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</Attribute>
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### aggregation
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<Attribute id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.aggregation">
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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.
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**Type**
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float | Callable\[\[list\[tuple\[float, complex]], float] | None
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</Attribute>
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### callback
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<Attribute id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.callback">
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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.
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**Type**
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Callable\[\[int, np.ndarray, float, dict\[str, Any]], None] | None
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</Attribute>
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**References**
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**\[1]: Barkoutsos, P. K., Nannicini, G., Robert, A., Tavernelli, I., and Woerner, S.,**
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“Improving Variational Quantum Optimization using CVaR” [arXiv:1907.04769](https://arxiv.org/abs/1907.04769)
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**Parameters**
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* **sampler** ([*BaseSampler*](qiskit.primitives.BaseSampler "qiskit.primitives.BaseSampler")) – The sampler primitive to sample the circuits.
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* **ansatz** ([*QuantumCircuit*](qiskit.circuit.QuantumCircuit "qiskit.circuit.QuantumCircuit")) – A parameterized quantum circuit to prepare the trial state.
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* **optimizer** ([*Optimizer*](qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.Optimizer") *|*[*Minimizer*](qiskit.algorithms.optimizers.Minimizer "qiskit.algorithms.optimizers.Minimizer")) – A classical optimizer to find the minimum energy. This can either be a Qiskit [`Optimizer`](qiskit.algorithms.optimizers.Optimizer "qiskit.algorithms.optimizers.Optimizer") or a callable implementing the [`Minimizer`](qiskit.algorithms.optimizers.Minimizer "qiskit.algorithms.optimizers.Minimizer") protocol.
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* **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`](#qiskit.algorithms.minimum_eigensolvers.SamplingVQE.ansatz "qiskit.algorithms.minimum_eigensolvers.SamplingVQE.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.
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* **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.
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* **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.
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## Methods
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### compute\_minimum\_eigenvalue
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<Function id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.compute_minimum_eigenvalue" signature="SamplingVQE.compute_minimum_eigenvalue(operator, aux_operators=None)">
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Compute the minimum eigenvalue of a diagonal operator.
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**Parameters**
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* **operator** (*BaseOperator |* [*PauliSumOp*](qiskit.opflow.primitive_ops.PauliSumOp "qiskit.opflow.primitive_ops.PauliSumOp")) – Diagonal qubit operator.
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* **aux\_operators** (*ListOrDict\[BaseOperator |* [*PauliSumOp*](qiskit.opflow.primitive_ops.PauliSumOp "qiskit.opflow.primitive_ops.PauliSumOp")*] | None*) – Optional list of auxiliary operators to be evaluated with the final state.
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**Return type**
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[SamplingMinimumEigensolverResult](qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolverResult "qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolverResult")
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**Returns**
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A [`SamplingMinimumEigensolverResult`](qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolverResult "qiskit.algorithms.minimum_eigensolvers.SamplingMinimumEigensolverResult") containing the optimization result.
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</Function>
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### supports\_aux\_operators
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<Function id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.supports_aux_operators" signature="SamplingVQE.supports_aux_operators()" modifiers="classmethod">
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Whether computing the expectation value of auxiliary operators is supported.
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If the minimum eigensolver computes an eigenstate of the main operator then it can compute the expectation value of the aux\_operators for that state. Otherwise they will be ignored.
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**Return type**
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`bool`
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**Returns**
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True if aux\_operator expectations can be evaluated, False otherwise
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</Function>
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## Attributes
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### initial\_point
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<Attribute id="qiskit.algorithms.minimum_eigensolvers.SamplingVQE.initial_point">
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Return the initial point.
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</Attribute>
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</Class>
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