53 lines
3.2 KiB
Plaintext
53 lines
3.2 KiB
Plaintext
---
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title: RealMcLachlanPrinciple
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description: API reference for qiskit.algorithms.time_evolvers.variational.RealMcLachlanPrinciple
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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.time_evolvers.variational.RealMcLachlanPrinciple
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---
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# RealMcLachlanPrinciple
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<Class id="qiskit.algorithms.time_evolvers.variational.RealMcLachlanPrinciple" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.25/qiskit/algorithms/time_evolvers/variational/variational_principles/real_mc_lachlan_principle.py" signature="qiskit.algorithms.time_evolvers.variational.RealMcLachlanPrinciple(qgt=None, gradient=None)" modifiers="class">
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Bases: [`RealVariationalPrinciple`](qiskit.algorithms.time_evolvers.variational.RealVariationalPrinciple "qiskit.algorithms.time_evolvers.variational.variational_principles.real_variational_principle.RealVariationalPrinciple")
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Class for a Real McLachlan’s Variational Principle. It aims to minimize the distance between both sides of the Schrödinger equation with a quantum state given as a parametrized trial state. The principle leads to a system of linear equations handled by a linear solver. The real variant means that we consider real time dynamics.
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**Parameters**
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* **qgt** ([*BaseQGT*](qiskit.algorithms.gradients.BaseQGT "qiskit.algorithms.gradients.BaseQGT") *| None*) – Instance of a the GQT class used to compute the QFI. If `None` provided, `LinCombQGT` is used.
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* **gradient** ([*BaseEstimatorGradient*](qiskit.algorithms.gradients.BaseEstimatorGradient "qiskit.algorithms.gradients.BaseEstimatorGradient") *| None*) – Instance of a class used to compute the state gradient. If `None` provided, `LinCombEstimatorGradient` is used.
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**Raises**
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[**AlgorithmError**](algorithms#qiskit.algorithms.AlgorithmError "qiskit.algorithms.AlgorithmError") – If the gradient instance does not contain an estimator.
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## Methods
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### evolution\_gradient
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<Function id="qiskit.algorithms.time_evolvers.variational.RealMcLachlanPrinciple.evolution_gradient" signature="evolution_gradient(hamiltonian, ansatz, param_values, gradient_params=None)">
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Calculates an evolution gradient according to the rules of this variational principle.
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**Parameters**
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* **hamiltonian** (*BaseOperator*) – Operator used for Variational Quantum Time Evolution.
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* **ansatz** ([*QuantumCircuit*](qiskit.circuit.QuantumCircuit "qiskit.circuit.QuantumCircuit")) – Quantum state in the form of a parametrized quantum circuit.
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* **param\_values** (*Sequence\[*[*float*](https://docs.python.org/3/library/functions.html#float "(in Python v3.12)")*]*) – Values of parameters to be bound.
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* **gradient\_params** (*Sequence\[*[*Parameter*](qiskit.circuit.Parameter "qiskit.circuit.Parameter")*] | None*) – List of parameters with respect to which gradients should be computed. If `None` given, gradients w\.r.t. all parameters will be computed.
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**Returns**
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An evolution gradient.
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**Raises**
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[**AlgorithmError**](algorithms#qiskit.algorithms.AlgorithmError "qiskit.algorithms.AlgorithmError") – If a gradient job fails.
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**Return type**
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np.ndarray
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</Function>
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</Class>
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