Rydberg Vision via frugal Quantum Image Fingerprinting

TL;DR

This paper presents a novel quantum image matching framework using Rydberg atom arrays, leveraging condensed-matter observables for efficient, scale-invariant image retrieval on analog quantum computers.

Contribution

It introduces a quantum-native image fingerprinting method combining classical pre-processing with quantum observables, enabling efficient image matching on neutral-atom quantum hardware.

Findings

Successful matching of industrial objects with fewer than 24 atoms

First use of static structure factor as an image descriptor in quantum computing

Demonstrates feasibility of quantum image fingerprinting with reduced data

Abstract

Gate-based quantum image processing is constrained by qubit scarcity and the high overhead of quantum state preparation, limiting its applicability to realistic geometric data. We introduce a quantum-native framework for image matching on neutral-atom analog quantum computers that advances our earlier Sparse-Dots Representation (SDR) approach. A classical pre-processing pipeline -- Sobel edge extraction followed by the Ramer--Douglas--Peucker (RDP) algorithm -- converts an input image into a geometrically faithful Sparse-Dots point cloud of substantially fewer atoms. This atom layout is virtually embedded into the programmable tweezer array of QuEra's Aquila device via its Bloqade SDK, where the image geometry is encoded physically in the distance-dependent van der Waals interaction term of the Rydberg Hamiltonian. After time-evolution, we extract the many-body fingerprint of each image using two observables -- the Pearson-normalized two-site correlation matrix which encodes the blockade-induced correlation structure of the quantum state, and the two-dimensional static structure factor evaluated on a fixed wavevector grid, yielding a fingerprint vector of constant length regardless of atom count. In Stage~1, image matching is performed by cosine similarity on the fingerprint vectors, a scale-invariant metric appropriate for Fourier-domain descriptors. In Stage~2, this approach is extended to quantum reservoir computing~(QRC) to enable machine learning via dramatically reduced training data and training cycles, as a preliminary proof-of-concept. Simulations using the Bloqade software stack confirm successful matching of industrial objects, often with fewer than 24 atoms. To our knowledge, this constitutes the first application of the static structure factor -- a condensed-matter quantum observable -- as an image retrieval descriptor in an analog quantum computing context.