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OSA Advanced Photonics Congress

Programmable Nanophotonics for Quantum Simulation and Machine Learning

Nicholas C. Harris, Yichen Shen, Gregory R. Steinbrecher, Mihika Prabhu, Tom W. Baehr-Jones, Michael Hochberg, Marin Soljacic A, Dirk Englund

Published Jan 1, 2017• 2 citations
Publisher

Abstract

Beyond communication, silicon photonics is becoming a promising platform for both quantum and classical information processing. Here, we discuss our recent results on using programmable nanophotonic systems to simulate quantum phenomena found in solid-state and biological systems as well as the implementation of optical neural networks for deep learning.

DOI
10.1364/iprsn.2017.itu3a.3

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Recent advances in materials, control, and nanofabrication now open the prospect for scalable quantum technologies based on solid-state quantum systems. In particular, photonic integrated circuits (PICs) now allow routing photons with high precision and low loss, and solid-state artificial atoms provide high-quality spin-photon interfaces. The first part of this talk will review progress towards quantum memory-integrated PICs for quantum networks and modular quantum computers. The second part of the talk will consider new directions for processing classical and quantum information in deep learning neural networks architectures.