Quantum Reservoir Computing Implementations for Classical and Quantum Problems

• URL: http://arxiv.org/abs/2211.08567v1
• Date: Tue, 15 Nov 2022 23:19:26 GMT
• Title: Quantum Reservoir Computing Implementations for Classical and Quantum Problems
• Authors: Adam Burgess and Marian Florescu
• Abstract summary: We employ a model open quantum system consisting of two-level atomic systems coupled to Lorentzian photonic cavities. We then deploy the quantum reservoir computing approach to an archetypal machine learning problem of image recognition.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this article we employ a model open quantum system consisting of two-level atomic systems coupled to Lorentzian photonic cavities, as an instantiation of a quantum physical reservoir computer. We then deployed the quantum reservoir computing approach to an archetypal machine learning problem of image recognition. We contrast the effectiveness of the quantum physical reservoir computer against a conventional approach using neural network of the similar architecture with the quantum physical reservoir computer layer removed. Remarkably, as the data set size is increased the quantum physical reservoir computer quickly starts out perform the conventional neural network. Furthermore, quantum physical reservoir computer provides superior effectiveness against number of training epochs at a set data set size and outperformed the neural network approach at every epoch number sampled. Finally, we have deployed the quantum physical reservoir computer approach to explore the quantum problem associated with the dynamics of open quantum systems in which an atomic system ensemble interacts with a structured photonic reservoir associated with a photonic band gap material. Our results demonstrate that the quantum physical reservoir computer is equally effective in generating useful representations for quantum problems, even with limited training data size.

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