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[2602.14641] Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset
Summary
This paper explores Quantum Reservoir Computing (QRC) using neutral atoms to enhance predictions in medical datasets, demonstrating improved accuracy and stability over classical methods.
Why It Matters
As medical datasets often present challenges like nonlinearity and limited size, this research highlights the potential of quantum computing as a viable alternative to classical machine learning, potentially transforming predictive analytics in healthcare.
Key Takeaways
- Quantum Reservoir Computing shows promise in improving prediction accuracy for medical datasets.
- Hardware execution of QRC provides greater stability and robustness compared to classical methods.
- The study indicates a regularizing effect from hardware execution, leading to better performance metrics.
Quantum Physics arXiv:2602.14641 (quant-ph) [Submitted on 16 Feb 2026] Title:Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset Authors:Luke Antoncich, Yuben Moodley, Ugo Varetto, Jingbo Wang, Jonathan Wurtz, Jing Chen, Pascal Jahan Elahi, Casey R. Myers View a PDF of the paper titled Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset, by Luke Antoncich and 7 other authors View PDF HTML (experimental) Abstract:Biomarker-based prediction of clinical outcomes is challenging due to nonlinear relationships, correlated features, and the limited size of many medical datasets. Classical machine-learning methods can struggle under these conditions, motivating the search for alternatives. In this work, we investigate quantum reservoir computing (QRC), using both noiseless emulation and hardware execution on the neutral-atom Rydberg processor \textit{Aquila}. We evaluate performance with six classical machine-learning models and use SHAP to generate feature subsets. We find that models trained on emulated quantum features achieve mean test accuracies comparable to those trained on classical features, but have higher training accuracies and greater variability over data splits, consistent with overfitting. When comparing hardware execution of QRC to noiseless emulation, the models are more robust over different data splits and often exhibit statistically significant improvements in mean test accuracy. This combination ...
