80 lines
2.9 KiB
Plaintext
80 lines
2.9 KiB
Plaintext
---
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title: DefaultCNOTUnitObjective
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description: API reference for qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective
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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.transpiler.synthesis.aqc.DefaultCNOTUnitObjective
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---
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# DefaultCNOTUnitObjective
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<Class id="qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective" isDedicatedPage={true} github="https://github.com/qiskit/qiskit/tree/stable/0.25/qiskit/transpiler/synthesis/aqc/cnot_unit_objective.py" signature="qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective(num_qubits, cnots)" modifiers="class">
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Bases: [`CNOTUnitObjective`](qiskit.transpiler.synthesis.aqc.CNOTUnitObjective "qiskit.transpiler.synthesis.aqc.cnot_unit_objective.CNOTUnitObjective")
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A naive implementation of the objective function based on CNOT units.
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**Parameters**
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* **num\_qubits** ([*int*](https://docs.python.org/3/library/functions.html#int "(in Python v3.12)")) – number of qubits.
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* **cnots** (*np.ndarray*) – a CNOT structure to be used in the optimization procedure.
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## Attributes
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### num\_cnots
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<Attribute id="qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective.num_cnots">
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Returns: A number of CNOT units to be used by the approximate circuit.
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</Attribute>
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### num\_thetas
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<Attribute id="qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective.num_thetas">
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Returns: Number of parameters (angles) of rotation gates in this circuit.
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</Attribute>
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### target\_matrix
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<Attribute id="qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective.target_matrix">
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Returns: a matrix being approximated
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</Attribute>
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## Methods
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### gradient
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<Function id="qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective.gradient" signature="gradient(param_values)">
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Computes a gradient with respect to parameters given a vector of parameter values.
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**Parameters**
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**param\_values** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.26)")) – a vector of parameter values for the optimization problem.
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**Returns**
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an array of gradient values.
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**Return type**
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[*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.26)")
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</Function>
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### objective
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<Function id="qiskit.transpiler.synthesis.aqc.DefaultCNOTUnitObjective.objective" signature="objective(param_values)">
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Computes a value of the objective function given a vector of parameter values.
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**Parameters**
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**param\_values** ([*ndarray*](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v1.26)")) – a vector of parameter values for the optimization problem.
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**Returns**
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a float value of the objective function.
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**Return type**
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[*SupportsFloat*](https://docs.python.org/3/library/typing.html#typing.SupportsFloat "(in Python v3.12)")
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</Function>
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</Class>
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