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Trapped Ion Quantum Computing Superconducting Qubits Quantum Machine Learning Quantum Simulation

A quantum analytical Adam descent through parameter shift rule using Qibo

arXiv
Authors: Matteo Robbiati, Stavros Efthymiou, Andrea Pasquale, Stefano Carrazza

Year

2022

Paper ID

58231

Status

Preprint

Abstract Read

~2 min

Abstract Words

80

Citations

N/A

Abstract

In this proceedings we present quantum machine learning optimization experiments using stochastic gradient descent with the parameter shift rule algorithm. We first describe the gradient evaluation algorithm and its optimization procedure implemented using the Qibo framework. After numerically testing the implementation using quantum simulation on classical hardware, we perform successfully a full quantum hardware optimization exercise using a single superconducting qubit chip controlled by Qibo. We show results for a quantum regression model by comparing simulation to real hardware optimization.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2022 reference point for readers tracking recent quantum research.
  • In this proceedings we present quantum machine learning optimization experiments using stochastic gradient descent with the parameter shift rule algorithm.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2210.10787 [2] arXiv https://arxiv.org/abs/2210.10787 [3] Publisher https://arxiv.org/abs/2210.10787

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