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.

A quantum analytical Adam descent through parameter shift rule using Qibo / M. Robbiati, S. Efthymiou, A. Pasquale, S. Carrazza. ((Intervento presentato al 41. convegno International Conference on High Energy Physics ICHEP tenutosi a Bologna : 6-13 luglio nel 2022.

A quantum analytical Adam descent through parameter shift rule using Qibo

A. Pasquale;S. Carrazza
2022

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.
Quantum Physics; Quantum Physics; High Energy Physics - Phenomenology
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
http://arxiv.org/abs/2210.10787v1
A quantum analytical Adam descent through parameter shift rule using Qibo / M. Robbiati, S. Efthymiou, A. Pasquale, S. Carrazza. ((Intervento presentato al 41. convegno International Conference on High Energy Physics ICHEP tenutosi a Bologna : 6-13 luglio nel 2022.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/943547
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