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