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  • Open Access

Connection between Memory Performance and Optical Absorption in Quantum Reservoir Computing

Niclas Götting1,*, Steffen Wilksen1, Alexander Steinhoff1, Frederik Lohof2, and Christopher Gies1

  • *Contact author: niclas.goetting@uol.de
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Phys. Rev. Lett. 135, 240403 – Published 9 December, 2025

DOI: https://doi.org/10.1103/vp79-8t1l

Abstract

Quantum reservoir computing (QRC) offers a promising paradigm for harnessing quantum systems for machine learning tasks, especially in the era of noisy intermediate-scale quantum devices. While information-theoretical benchmarks like short-term memory capacity (STMC) are widely used to evaluate QRC performance, they fail to provide insights into the physical mechanisms underlying these quantum neural networks. We establish a quantitative connection between the optical absorption spectrum of a quantum reservoir and its memory performance, revealing that optimal STMC aligns directly with maximal absorption, providing a physical explanation for the previously reported “sweet-spot” behavior in QRC performance as a function of dissipation. This connection bridges quantum information theory with experimentally accessible physical properties, opening pathways for targeted engineering of quantum reservoir computers with optimized performance for specific tasks.

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Phys. Rev. Lett. 135, 240403– Published 9 December, 2025
  • Received 12 February 2025
  • Revised 15 May 2025
  • Accepted 14 November 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

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