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---
title: VQEAdapt
description: API reference for qiskit.chemistry.algorithms.VQEAdapt
in_page_toc_min_heading_level: 1
python_api_type: class
python_api_name: qiskit.chemistry.algorithms.VQEAdapt
---
# qiskit.chemistry.algorithms.VQEAdapt
<Class id="qiskit.chemistry.algorithms.VQEAdapt" isDedicatedPage={true} github="https://github.com/qiskit-community/qiskit-aqua/tree/stable/0.9/qiskit/chemistry/algorithms/minimum_eigen_solvers/vqe_adapt.py" signature="VQEAdapt(operator, var_form_base, optimizer, initial_point=None, excitation_pool=None, threshold=1e-05, delta=1, max_iterations=None, max_evals_grouped=1, aux_operators=None, quantum_instance=None)" modifiers="class">
DEPRECATED. The Adaptive VQE algorithm.
See [https://arxiv.org/abs/1812.11173](https://arxiv.org/abs/1812.11173)
**Parameters**
* **operator** (`LegacyBaseOperator`) – Qubit operator
* **var\_form\_base** (`VariationalForm`) – base parameterized variational form
* **optimizer** (`Optimizer`) – the classical optimizer algorithm
* **initial\_point** (`Optional`\[`ndarray`]) – optimizer initial point
* **excitation\_pool** (`Optional`\[`List`\[`WeightedPauliOperator`]]) – list of excitation operators
* **threshold** (`float`) – absolute threshold value for gradients, has a min. value of 1e-15.
* **delta** (`float`) – finite difference step size for gradient computation, has a min. value of 1e-5.
* **max\_iterations** (`Optional`\[`int`]) – maximum number of macro iterations of the VQEAdapt algorithm.
* **max\_evals\_grouped** (`int`) – max number of evaluations performed simultaneously
* **aux\_operators** (`Optional`\[`List`\[`LegacyBaseOperator`]]) – Auxiliary operators to be evaluated at each eigenvalue
* **quantum\_instance** (`Union`\[`QuantumInstance`, `Backend`, `BaseBackend`, `None`]) – Quantum Instance or Backend
**Raises**
* **ValueError** – if var\_form\_base is not an instance of UCCSD.
* **See also** – qiskit/chemistry/components/variational\_forms/uccsd\_adapt.py
### \_\_init\_\_
<Function id="qiskit.chemistry.algorithms.VQEAdapt.__init__" signature="__init__(operator, var_form_base, optimizer, initial_point=None, excitation_pool=None, threshold=1e-05, delta=1, max_iterations=None, max_evals_grouped=1, aux_operators=None, quantum_instance=None)">
**Parameters**
* **operator** (`LegacyBaseOperator`) – Qubit operator
* **var\_form\_base** (`VariationalForm`) – base parameterized variational form
* **optimizer** (`Optimizer`) – the classical optimizer algorithm
* **initial\_point** (`Optional`\[`ndarray`]) – optimizer initial point
* **excitation\_pool** (`Optional`\[`List`\[`WeightedPauliOperator`]]) – list of excitation operators
* **threshold** (`float`) – absolute threshold value for gradients, has a min. value of 1e-15.
* **delta** (`float`) – finite difference step size for gradient computation, has a min. value of 1e-5.
* **max\_iterations** (`Optional`\[`int`]) – maximum number of macro iterations of the VQEAdapt algorithm.
* **max\_evals\_grouped** (`int`) – max number of evaluations performed simultaneously
* **aux\_operators** (`Optional`\[`List`\[`LegacyBaseOperator`]]) – Auxiliary operators to be evaluated at each eigenvalue
* **quantum\_instance** (`Union`\[`QuantumInstance`, `Backend`, `BaseBackend`, `None`]) – Quantum Instance or Backend
**Raises**
* **ValueError** – if var\_form\_base is not an instance of UCCSD.
* **See also** – qiskit/chemistry/components/variational\_forms/uccsd\_adapt.py
</Function>
## Methods
| | |
| -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |
| [`__init__`](#qiskit.chemistry.algorithms.VQEAdapt.__init__ "qiskit.chemistry.algorithms.VQEAdapt.__init__")(operator, var\_form\_base, optimizer) | **type operator**`LegacyBaseOperator` |
| [`cleanup_parameterized_circuits`](#qiskit.chemistry.algorithms.VQEAdapt.cleanup_parameterized_circuits "qiskit.chemistry.algorithms.VQEAdapt.cleanup_parameterized_circuits")() | set parameterized circuits to None |
| [`find_minimum`](#qiskit.chemistry.algorithms.VQEAdapt.find_minimum "qiskit.chemistry.algorithms.VQEAdapt.find_minimum")(\[initial\_point, var\_form, …]) | Optimize to find the minimum cost value. |
| [`get_optimal_circuit`](#qiskit.chemistry.algorithms.VQEAdapt.get_optimal_circuit "qiskit.chemistry.algorithms.VQEAdapt.get_optimal_circuit")() | get optimal circuit |
| [`get_optimal_cost`](#qiskit.chemistry.algorithms.VQEAdapt.get_optimal_cost "qiskit.chemistry.algorithms.VQEAdapt.get_optimal_cost")() | get optimal cost |
| [`get_optimal_vector`](#qiskit.chemistry.algorithms.VQEAdapt.get_optimal_vector "qiskit.chemistry.algorithms.VQEAdapt.get_optimal_vector")() | get optimal vector |
| [`get_prob_vector_for_params`](#qiskit.chemistry.algorithms.VQEAdapt.get_prob_vector_for_params "qiskit.chemistry.algorithms.VQEAdapt.get_prob_vector_for_params")(…\[, …]) | Helper function to get probability vectors for a set of params |
| [`get_probabilities_for_counts`](#qiskit.chemistry.algorithms.VQEAdapt.get_probabilities_for_counts "qiskit.chemistry.algorithms.VQEAdapt.get_probabilities_for_counts")(counts) | get probabilities for counts |
| [`run`](#qiskit.chemistry.algorithms.VQEAdapt.run "qiskit.chemistry.algorithms.VQEAdapt.run")(\[quantum\_instance]) | Execute the algorithm with selected backend. |
| [`set_backend`](#qiskit.chemistry.algorithms.VQEAdapt.set_backend "qiskit.chemistry.algorithms.VQEAdapt.set_backend")(backend, \*\*kwargs) | Sets backend with configuration. |
## Attributes
| | |
| ------------------------------------------------------------------------------------------------------------------------------------ | -------------------------- |
| [`backend`](#qiskit.chemistry.algorithms.VQEAdapt.backend "qiskit.chemistry.algorithms.VQEAdapt.backend") | Returns backend. |
| [`initial_point`](#qiskit.chemistry.algorithms.VQEAdapt.initial_point "qiskit.chemistry.algorithms.VQEAdapt.initial_point") | Returns initial point |
| [`optimal_params`](#qiskit.chemistry.algorithms.VQEAdapt.optimal_params "qiskit.chemistry.algorithms.VQEAdapt.optimal_params") | returns optimal parameters |
| [`optimizer`](#qiskit.chemistry.algorithms.VQEAdapt.optimizer "qiskit.chemistry.algorithms.VQEAdapt.optimizer") | Returns optimizer |
| [`quantum_instance`](#qiskit.chemistry.algorithms.VQEAdapt.quantum_instance "qiskit.chemistry.algorithms.VQEAdapt.quantum_instance") | Returns quantum instance. |
| [`random`](#qiskit.chemistry.algorithms.VQEAdapt.random "qiskit.chemistry.algorithms.VQEAdapt.random") | Return a numpy random. |
| [`var_form`](#qiskit.chemistry.algorithms.VQEAdapt.var_form "qiskit.chemistry.algorithms.VQEAdapt.var_form") | Returns variational form |
### backend
<Attribute id="qiskit.chemistry.algorithms.VQEAdapt.backend">
Returns backend.
**Return type**
`Union`\[`Backend`, `BaseBackend`]
</Attribute>
### cleanup\_parameterized\_circuits
<Function id="qiskit.chemistry.algorithms.VQEAdapt.cleanup_parameterized_circuits" signature="cleanup_parameterized_circuits()">
set parameterized circuits to None
</Function>
### find\_minimum
<Function id="qiskit.chemistry.algorithms.VQEAdapt.find_minimum" signature="find_minimum(initial_point=None, var_form=None, cost_fn=None, optimizer=None, gradient_fn=None)">
Optimize to find the minimum cost value.
**Parameters**
* **initial\_point** (`Optional`\[`ndarray`]) – If not None will be used instead of any initial point supplied via constructor. If None and None was supplied to constructor then a random point will be used if the optimizer requires an initial point.
* **var\_form** (`Union`\[`QuantumCircuit`, `VariationalForm`, `None`]) – If not None will be used instead of any variational form supplied via constructor.
* **cost\_fn** (`Optional`\[`Callable`]) – If not None will be used instead of any cost\_fn supplied via constructor.
* **optimizer** (`Optional`\[`Optimizer`]) – If not None will be used instead of any optimizer supplied via constructor.
* **gradient\_fn** (`Optional`\[`Callable`]) – Optional gradient function for optimizer
**Returns**
Optimized variational parameters, and corresponding minimum cost value.
**Return type**
dict
**Raises**
**ValueError** – invalid input
</Function>
### get\_optimal\_circuit
<Function id="qiskit.chemistry.algorithms.VQEAdapt.get_optimal_circuit" signature="get_optimal_circuit()">
get optimal circuit
</Function>
### get\_optimal\_cost
<Function id="qiskit.chemistry.algorithms.VQEAdapt.get_optimal_cost" signature="get_optimal_cost()">
get optimal cost
</Function>
### get\_optimal\_vector
<Function id="qiskit.chemistry.algorithms.VQEAdapt.get_optimal_vector" signature="get_optimal_vector()">
get optimal vector
</Function>
### get\_prob\_vector\_for\_params
<Function id="qiskit.chemistry.algorithms.VQEAdapt.get_prob_vector_for_params" signature="get_prob_vector_for_params(construct_circuit_fn, params_s, quantum_instance, construct_circuit_args=None)">
Helper function to get probability vectors for a set of params
</Function>
### get\_probabilities\_for\_counts
<Function id="qiskit.chemistry.algorithms.VQEAdapt.get_probabilities_for_counts" signature="get_probabilities_for_counts(counts)">
get probabilities for counts
</Function>
### initial\_point
<Attribute id="qiskit.chemistry.algorithms.VQEAdapt.initial_point">
Returns initial point
**Return type**
`Optional`\[`ndarray`]
</Attribute>
### optimal\_params
<Attribute id="qiskit.chemistry.algorithms.VQEAdapt.optimal_params">
returns optimal parameters
</Attribute>
### optimizer
<Attribute id="qiskit.chemistry.algorithms.VQEAdapt.optimizer">
Returns optimizer
**Return type**
`Optional`\[`Optimizer`]
</Attribute>
### quantum\_instance
<Attribute id="qiskit.chemistry.algorithms.VQEAdapt.quantum_instance">
Returns quantum instance.
**Return type**
`Optional`\[`QuantumInstance`]
</Attribute>
### random
<Attribute id="qiskit.chemistry.algorithms.VQEAdapt.random">
Return a numpy random.
</Attribute>
### run
<Function id="qiskit.chemistry.algorithms.VQEAdapt.run" signature="run(quantum_instance=None, **kwargs)">
Execute the algorithm with selected backend.
**Parameters**
* **quantum\_instance** (`Union`\[`QuantumInstance`, `Backend`, `BaseBackend`, `None`]) – the experimental setting.
* **kwargs** (*dict*) – kwargs
**Returns**
results of an algorithm.
**Return type**
dict
**Raises**
[**AquaError**](qiskit.aqua.AquaError "qiskit.aqua.AquaError") – If a quantum instance or backend has not been provided
</Function>
### set\_backend
<Function id="qiskit.chemistry.algorithms.VQEAdapt.set_backend" signature="set_backend(backend, **kwargs)">
Sets backend with configuration.
**Return type**
`None`
</Function>
### var\_form
<Attribute id="qiskit.chemistry.algorithms.VQEAdapt.var_form">
Returns variational form
**Return type**
`Union`\[`QuantumCircuit`, `VariationalForm`, `None`]
</Attribute>
</Class>
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