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arXiv:2108.13213

Supervised Learning by Chiral-Network-Based Photonic Quantum Computing

Wei‐Bin Yan, Yingjie Zhang, Zhong‐Xiao Man, Heng Fan, Yun‐Jie Xia

Published Aug 30, 2021• 0 citations
arXivHTMLPDF

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.

DOI
10.48550/arxiv.2108.13213

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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.
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Inspired by the excellent control of single photons realized by the atom-photon-chiral couplings, we propose a novel potential photonic-quantum-computation scheme. The single-photon rotating and phase-shift operations, which can be controlled by another single photon, are realized by properly designed atom-photon-chiral couplings. The operations can be integrated into a chiral quantum network to realize photonic quantum computation. Based on the proposal, an algorithm to perform the machine learning tasks is developed, in which the essential nonlinearities come from the appropriately designed operations.
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Machine learning techniques have witnessed remarkable advancements in recent years, and quantum computing has emerged as a promising avenue to enhance these capabilities. This chapter, explores the integration of supervised quantum models into the realm of machine learning. A comprehensive overview of the foundational concepts underlying quantum computing and its potential applications in supervised learning tasks is presented. Specifically, the focus is on quantum support vector machines, quantum neural networks, quantum Boltzmann machine, and the like, elucidating their principles and potential advantages over classical counterparts. Moreover, the chapter addresses the challenges posed by the implementation of these quantum algorithms and highlights the current state of quantum hardware in tackling practical machine-learning problems. Ultimately, this work paves the way for harnessing quantum computational power to foster advancements in the field of machine learning.