Quantum learning by measurement and feedback

TL;DR

This paper introduces a quantum learning approach where quantum gate parameters are adjusted via feedback to improve computational tasks, demonstrating feasibility through simulations on search and factoring algorithms.

Contribution

It presents a novel quantum learning method using measurement and feedback to optimize gate parameters for scalable quantum computing.

Findings

Feasibility shown through simulations on search algorithms

Successful demonstration on factoring algorithms

Scalable approach for large quantum problems

Abstract

We propose an approach to quantum computing in which quantum gate strengths are parametrized by quantum degrees of freedom, and the capability of the quantum computer to perform desired tasks is monitored and gradually improved by successive feedback modifications of the coupling strength parameters. Our proposal aims at experimental implementation, scalable to computational problems too large to be simulated theoretically, and we demonstrate feasibility of our proposal with simulations on search and factoring algorithms.