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This research investigates the intersection of reservoir computing and quantum computing, emphasizing their synergistic potential.

March 2, 2026Open Access

From quantum feature maps to quantum reservoir computing: an applicative perspective

Key Points

This research investigates the intersection of reservoir computing and quantum computing, emphasizing their synergistic potential.
Examined the capabilities of quantum systems as reservoirs for machine learning tasks.
Focus on neutral-atom quantum processing units to illustrate the quantum reservoir computing workflow.
Discussed challenges and potential applications for advancing reservoir computing.
Identified quantum systems as capable reservoirs for machine learning tasks.
Demonstrated a novel workflow for implementing quantum reservoir computing using neutral atoms.
Outlined key challenges for future development and application of quantum reservoir computing.

Abstract

Abstract 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’.

From quantum feature maps to quantum reservoir computing: an applicative perspective

Key Points

Abstract

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