Image Denoising via Quantum Reservoir Computing

TL;DR

This paper introduces a hybrid quantum-classical approach using Quantum Reservoir Computing with Rydberg atom arrays for image denoising, demonstrating improved image quality and practical implementation on a neutral-atom quantum processor.

Contribution

It presents a novel quantum reservoir computing framework for image denoising, integrating quantum dynamics with classical neural networks, and validates it on a real quantum hardware platform.

Findings

QRC-based model improves image sharpness over classical baseline.

The approach effectively captures complex correlations in images.

Experimental validation on QuEra's Aquila processor shows practical feasibility.

Abstract

Quantum Reservoir Computing (QRC) leverages the natural dynamics of quantum systems for information processing, without requiring a fault-tolerant quantum computer. In this work, we apply QRC within a hybrid quantum classical framework for image denoising. The quantum reservoir is implemented using a Rydberg atom array, while a classical neural network serves as the readout layer. To prepare the input, images are first compressed using Principal Component Analysis (PCA), reducing their dimensionality to match the size of the atom array. Each feature vector is encoded into local detuning parameters of a time-dependent Hamiltonian governing the Rydberg system. As the system evolves, it generates nonlinear embeddings through the measurement of observables across multiple time steps. These temporal embeddings capture complex correlations, which are fed into a classical neural network to reconstruct the denoised images. To evaluate performance, we compare this QRC-assisted model against a baseline architecture consisting of PCA followed by a dense neural network, trained under identical conditions. Our results show that the QRC-based approach achieves improved image sharpness and similar structural recovery compared to the PCA-based model. We demonstrate the practical viability of this framework through experiments on QuEra's Aquila neutral-atom processor, leveraging its programmable atom arrays to physically realize the reservoir dynamics.