Skip to main contentIBM Quantum Documentation

Objective function(s)

qiskit_addon_aqc_tensor.objective

Code for building and evaluating objective functions used for AQC parameter optimization.

Currently, this module provides the simplest possible objective function, OneMinusFidelity.

OneMinusFidelity

class OneMinusFidelity(target, ansatz, settings)[source]

GitHub

Bases: object

Simplest possible objective function for use with AQC-Tensor.

Its definition is given by Eq. (7) in arXiv:2301.08609v6:

C=10V(θ)ψtarget2.C = 1 - \left| \langle 0 | V^{\dagger}(\vec\theta) | \psi_\mathrm{target} \rangle \right|^2 .

Minimizing this function is equivalent to maximizing the pure-state fidelity between the state prepared by the ansatz circuit at the current parameter point,(V(θ)0V(\vec\theta) |0\rangle, and the target state, ψtarget| \psi_\mathrm{target} \rangle.

When called with an ndarray of parameters, this object will return (objective_value, gradient) as a tuple[float, numpy.ndarray].

Initialize the objective function.

Parameters

__call__

__call__(x)[source]

GitHub

Evaluate (objective_value, gradient) of function at point x.

Return type

tuple[float, ndarray]

Parameters

x (ndarray)

target

Type: TensorNetworkState

Target tensor network.

Was this page helpful?
Report a bug or request content on GitHub.