Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning

• URL: http://arxiv.org/abs/2510.13473v2
• Date: Tue, 21 Oct 2025 11:39:28 GMT
• Title: Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning
• Authors: Shehbaz Tariq, Muhammad Talha, Symeon Chatzinotas, Hyundong Shin,
• Abstract summary: Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
• Score: 45.92935470813908
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum reservoir computing (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems for extracting spatiotemporal patterns in sequential and time-series data with minimal training overhead. Although QRC inherits the expressive capabilities associated with quantum encodings, recent studies indicate that quantum classifiers based on variational circuits remain susceptible to adversarial perturbations. In this perspective, we investigate the first systematic evaluation of adversarial robustness in a QRC based learning model. Our reservoir comprises an array of strongly interacting Rydberg atoms governed by a fixed Hamiltonian, which naturally evolves under complex quantum dynamics, producing high-dimensional embeddings. A lightweight multilayer perceptron serves as the trainable readout layer. We utilize the balanced datasets, namely MNIST, Fashion-MNIST, and Kuzushiji-MNIST, as a benchmark for rigorously evaluating the impact of augmenting the quantum reservoir with a Multilayer perceptron (MLP) in white-box adversarial attacks to assess its robustness. We demonstrate that this approach yields significantly higher accuracy than purely classical models across all perturbation strengths tested. This hybrid approach reveals a new source of quantum advantage and provides practical guidance for the secure deployment of machine learning models on quantum-centric supercomputing with near-term hardware.

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