Supervised Quantum Learning without Measurements

TL;DR

This paper introduces a measurement-free quantum machine learning algorithm that uses time-delayed feedback to efficiently solve problems encoded in quantum controlled unitaries, potentially expanding quantum tech applications.

Contribution

It presents a novel quantum learning protocol leveraging time-delayed equations to avoid measurements, enhancing quantum machine learning techniques.

Findings

Numerical simulations show competitive performance with classical methods.

The protocol eliminates the need for intermediate measurements.

Time-delayed feedback improves quantum learning efficiency.

Abstract

We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.