From quantum feature maps to quantum reservoir computing: an applicative perspective
We explore the interplay between two emerging paradigms: reservoir computing (RC) and quantum computing (QC). We observe how quantum systems featuring beyond-classical correlations and vast computational spaces can serve as non-trivial, experimentally viable reservoirs for typical tasks in machine learning. With a focus on neutral-atom quantum processing units, we describe and exemplify a novel quantum RC (QRC) workflow. We conclude by exploratively discussing the main challenges ahead, while arguing how QRC can offer a natural candidate to push forward RC applications. This article is part of the discussion meeting issue 'Bits, neurons and qubits for sustainable AI'.
Authors
- Casper Gyurik (ORCID: https://orcid.org/0000-0001-8311-9215)
- Antonio Sannia (ORCID: https://orcid.org/0009-0009-8368-1019)
- Antonio A. Gentile (ORCID: https://orcid.org/0000-0002-1763-9746)
- Evan Philip (ORCID: https://orcid.org/0000-0001-5405-2306)
- Filip Wudarski (ORCID: https://orcid.org/0000-0002-0911-8342)
- Oleksandr Kyriienko
- H. Sadeghi (ORCID: https://orcid.org/0000-0002-8772-951X)
- Davide Venturelli
Institutions
- Consejo Superior de Investigaciones Científicas (ES)
- University of the South Pacific - Solomon Islands Campus (SB)
- Pennsylvania Academy of Science (US)
- University of Sheffield (GB)
Publication Details
- Journal
- Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
- Published
- 2026-02-26
- DOI
- https://doi.org/10.1098/rsta.2025.0085
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00