Strategy for quantum algorithm design assisted by machine learning

TL;DR

This paper introduces a hybrid quantum-classical machine learning approach to design quantum algorithms, demonstrated on the Deutsch-Jozsa problem, showing efficient learning with a square root dependence on parameters.

Contribution

It presents a novel quantum algorithm design method using a quantum-classical hybrid simulator, improving learning efficiency over classical machine learning approaches.

Findings

Successfully learned quantum algorithm for Deutsch-Jozsa problem

Learning time scales with the square root of parameters

Method applicable to oracle-based quantum algorithms

Abstract

We propose a method for quantum algorithm design assisted by machine learning. The method uses a quantum-classical hybrid simulator, where a "quantum student" is being taught by a "classical teacher." In other words, in our method, the learning system is supposed to evolve into a quantum algorithm for a given problem assisted by classical main-feedback system. Our method is applicable to design quantum oracle-based algorithm. As a case study, we chose an oracle decision problem, called a Deutsch-Jozsa problem. We showed by using Monte-Carlo simulations that our simulator can faithfully learn quantum algorithm to solve the problem for given oracle. Remarkably, learning time is proportional to the square root of the total number of parameters instead of the exponential dependance found in the classical machine learning based method.