Quantum and classical processing with photonic quantum machine learning

TL;DR

This paper demonstrates a scalable photonic quantum reservoir computing device capable of performing quantum and classical machine learning tasks, improving accuracy through imperfection mitigation.

Contribution

It introduces a programmable silicon photonic chip for quantum reservoir processing, enabling quantum state tomography and entanglement measurement with enhanced accuracy.

Findings

Successfully implemented quantum state tomography.

Achieved measurement of entanglement via negativity.

Significant accuracy improvement with imperfection mitigation.

Abstract

Artificial intelligence and machine learning have been widely adopted both in the industry and in everyday life, but at the cost of high compute demands. Recent studies show that implementing machine learning in physical systems in the deep quantum regime could not only lead to faster information processing, but also to perform tasks that are out of reach for classical systems. Here, we report a quantum reservoir processing device capable of performing both quantum and classical machine learning tasks. The implementation is realized with a programmable silicon chip excited with single photons, a highly scalable and adaptable photonics technology. We successfully implement a variety of quantum tasks, including quantum state tomography and measurement of entanglement via negativity. Moreover, we implement a method of mitigation of experimental imperfections which results in a significant improvement in accuracy in comparison to the same system operating in the classical regime. Our results demonstrate a method to overcome a crucial bottleneck of quantum technologies by providing a practical way of probing quantum states.