Exploring quantum mechanical advantage for reservoir computing

• URL: http://arxiv.org/abs/2302.03595v2
• Date: Thu, 11 May 2023 12:04:30 GMT
• Title: Exploring quantum mechanical advantage for reservoir computing
• Authors: Niclas G\"otting, Frederik Lohof, Christopher Gies
• Abstract summary: We establish a link between quantum properties of a quantum reservoir and its linear short-term memory performance. We find that a high degree of entanglement in the reservoir is a prerequisite for a more complex reservoir dynamics. We discuss the effect of dephasing in the performance of physical quantum reservoirs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum reservoir computing is an emerging field in machine learning with quantum systems. While classical reservoir computing has proven to be a capable concept of enabling machine learning on real, complex dynamical systems with many degrees of freedom, the advantage of its quantum analogue is yet to be fully explored. Here, we establish a link between quantum properties of a quantum reservoir, namely entanglement and its occupied phase space dimension, and its linear short-term memory performance. We find that a high degree of entanglement in the reservoir is a prerequisite for a more complex reservoir dynamics that is key to unlocking the exponential phase space and higher short-term memory capacity. We quantify these relations and discuss the effect of dephasing in the performance of physical quantum reservoirs.

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