Supervised Learning by Chiral-Network-Based Photonic Quantum Computing

TL;DR

This paper proposes a new photonic quantum computing scheme using chiral-network-based gates for supervised learning, demonstrating its effectiveness through numerical simulations of regression and classification tasks.

Contribution

It introduces a novel photonic quantum computation method leveraging atom-photon-chiral couplings for supervised learning, with demonstrated numerical performance.

Findings

Successful simulation of regression tasks

Effective classification performance

Potential for scalable photonic quantum learning

Abstract

Benefiting from the excellent control of single photons realized by the emitter-photon-chiral couplings, we propose a novel potential photonic-quantum-computation scheme to perform the supervised learning tasks. The gates for photonic quantum computation are realized by properly designed atom-photon-chiral couplings. The quantum algorithm of supervised learning, composed by integrating the realized gates, is implemented by the tunable gate parameters. The learning ability is demonstrated by numerically simulating the performance of regression and classification tasks.