A quantum analytical Adam descent through parameter shift rule using Qibo

TL;DR

This paper demonstrates quantum machine learning optimization using the parameter shift rule with Qibo, comparing simulation results to real hardware performance on a superconducting qubit system.

Contribution

It introduces an implementation of the parameter shift rule for quantum gradient evaluation within the Qibo framework and validates it on both simulated and real quantum hardware.

Findings

Successful quantum hardware optimization with a single qubit

Comparison between simulation and real hardware results

Effective use of the parameter shift rule in quantum machine learning

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.