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Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences

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

Casper Gyurik, Filip Wudarski, Evan Philip, Antonio Sannia, Hossein Sadeghi, Oleksandr Kyriienko, Davide Venturelli, Antonio A. Gentile

Published Feb 26, 2026• 0 citations
Publisher

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'.

DOI
10.1098/rsta.2025.0085

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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'.
We explore the interplay between two emerging paradigms: reservoir computing and quantum computing. 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 reservoir computing (QRC) workflow. We conclude exploratively discussing the main challenges ahead, whilst arguing how QRC can offer a natural candidate to push forward reservoir computing applications.
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In this thesis we investigate Quantum Reservoir Computing (QRC) as a non-variational paradigm for quantum machine learning on near-term neutral-atom devices. Motivated by the limitations of NISQ hardware, we implement a QRC pipeline in Pasqal’s Pulser environment that encodes classical inputs into detuning waveforms and extracts embeddings from Pauli-Z and ZZ observables. We design two encoding strategies: a global detuning applied to all atoms and a local detuning. Their performance is benchmarked against a classical PCA baseline using logistic regression. We find that QRC embeddings consistently outperform the classical features, with peak test accuracies of 90.17% (global) and 89.61% (local) compared to ≃ 85% for PCA. The advantage is especially marked in low-data regimes, where quantum dynamics enrich the feature space accessible to linear classifiers. We also identify practical challenges, such as symmetry-induced redundancy in global encoding and the absence of per-qubit detuning in current hardware. This establishes QRC as a promising and experimentally grounded framework to exploit the dynamical richness of neutral-atom platforms for supervised learning in the NISQ era.
Reservoir computing (RC) is an effective method for predicting chaotic systems by using a high-dimensional dynamic reservoir with fixed internal weights, while keeping the learning phase linear, which simplifies training and reduces computational complexity compared to fully trained recurrent neural networks (RNNs). Quantum reservoir computing (QRC) uses the exponential growth of Hilbert spaces in quantum systems, allowing for greater information processing, memory capacity, and computational power. However, the original QRC proposal requires coherent injection of inputs multiple times, complicating practical implementation. We present a hybrid quantum-classical approach that implements memory through classical post-processing of quantum measurements. This approach avoids the need for multiple coherent input injections and is evaluated on benchmark tasks, including the chaotic Mackey-Glass time series prediction. We tested our model on two physical platforms: a fully connected Ising model and a Rydberg atom array. The optimized model demonstrates promising predictive capabilities, achieving a higher number of steps compared to previously reported approaches.
Quantum Reservoir Computing (QRC) has emerged as a powerhouse for Noisy Intermediate-Scale Quantum (NISQ) machine learning, offering the ability to process complex temporal data with minimal training overhead by leveraging the high-dimensional dynamics of quantum states. This paper introduces QRC-Lab, an open-source, modular Python framework designed specifically to facilitate the transition from theoretical quantum dynamics to applied machine learning research. We provide a rigorous definition of QRC, contrasting physical and gate-based approaches, and detail the mathematical foundations of the reservoir mapping. QRC-Lab’s gate-based architecture is presented as a flexible laboratory for exploring data encoding, reservoir connectivity, and measurement strategies. We validate the framework through diverse case studies, including Short-Term Memory capacity, non-linear Parity check, and NARMA-type forecasting. Furthermore, we discuss the framework’s future roadmap towards multi-platform hardware support and its role in visualizing theoretical risk bounds, positioning QRC-Lab as a vital tool for the next generation of quantum computing education.