Quantum photonic processors to accelerate machine learning

Jacques Carolan

doi:10.1117/12.2610858

27 August 2021 Quantum photonic processors to accelerate machine learning

Jacques Carolan

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Proceedings Volume 11918, Photonics for Quantum 2020; 1191803 (2021) https://doi.org/10.1117/12.2610858
Event: Photonics for Quantum 2020, 2021, Online Only

Abstract

The generation and manipulation of quantum states of light has historically played a critical role in the development of quantum information science: from the first violation of Bell’s inequality to the more recent development of near-term quantum algorithms such as the variational quantum eigensolver. In this talk, I present a new frontier for photons at the intersection of quantum mechanics and machine learning. I will first provide a short introduction to the field of quantum photonics, then demonstrate how quantum photonic processors can accelerate both quantum and classical machine learning. Finally, I show how optimization techniques can enhance large-scale quantum control and provide a new path towards efficient verification of near-term quantum processors.

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Jacques Carolan "Quantum photonic processors to accelerate machine learning", Proc. SPIE 11918, Photonics for Quantum 2020, 1191803 (27 August 2021); https://doi.org/10.1117/12.2610858

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