Image Denoising via Quantum Reservoir Computing

TL;DR

This work introduces a hybrid quantum-classical image denoising framework based on Quantum Reservoir Computing (QRC) that uses PCA for dimensionality reduction, a Rydberg-atom reservoir to generate nonlinear temporal embeddings, and a classical readout network for reconstruction. Compared to a PCA-only baseline, the QRC approach achieves sharper edge restoration (higher TENG) while maintaining similar structural similarity (SSIM), with performance improving as the reservoir size increases. Experiments on MNIST with multiplicative speckle noise show promising gains, and hardware validation on QuEra's Aquila demonstrates feasibility, albeit with current limitations in shot count and coherence. The findings support the potential of quantum dynamical embeddings for vision tasks and motivate scaling studies on larger quantum devices and more diverse datasets.

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.

Figures (4)

• Figure 1: Schematic overview of the hybrid quantum–classical image denoising scheme based on Quantum Reservoir Computing (QRC). Noisy MNIST images undergo classical preprocessing (flattening and normalization) and PCA dimensionality reduction to d components (scaled to [0,1]), which are encoded as local detunings in a chain of d interacting Rydberg atoms. The quantum reservoir evolves under time-varying global Rabi pulses $\Omega(t)$ and detunings, and features are extracted from expectation values of single-site $\langle Z_i(t) \rangle$ and pairwise $\langle Z_i(t) Z_j(t) \rangle$ observables across multiple timesteps. A classical feedforward neural network reconstructs the clean image from these quantum embeddings using an MSE training objective. Compared to a classical PCA baseline, the QRC approach consistently produces sharper images while maintaining near-identical structure, with associated metrics increasing as the reservoir size increases up to the maximum simulated dimension of d=18 atoms.
• Figure 2: Comparison of denoising performance on MNIST dataset: The first row shows the noisy inputs, the second row shows the clean reference images, the third row shows results from Quantum Reservoir Computing (QRC) denoising using an 18-atom chain, and the fourth row shows outputs from classical PCA-based denoising. The QRC-denoised images have sharper edges than their classical counterparts, while maintaining similar structure.
• Figure 3: Comparison between Quantum Reservoir Computing (QRC) and classical PCA-based denoising performance across different reservoir sizes: The QRC model achieves a higher Tenegrad Sharpness (TENG) and slightly better Structural Similarity Index (SSIM) than the PCA model, while the PCA model achieves a lower (better) MSE. As the atom count increases both models continue to improve, with some evidence of saturation at the highest count. These results indicate the potential of QRC as a tool to denoise images and restore their sharpness, which has applications in medical contexts for example.
• Figure 4: Comparison of denoising performance between QRC embeddings generated by hardware and the PCA-based model: The first row shows results from Quantum Reservoir Computing (QRC) denoising using a 14-atom chain on the hardware device Aquila, and the second row shows outputs from classical PCA-based denoising. The QRC-denoised images have more or less similar sharpness to those generated by the PCA model.