Analog Quantum Image Representation with Qubit-Frugal Encoding
Vikrant Sharma, Neel Kanth Kundu
TL;DR
This work introduces Sparse Dot Representation (SDR), a qubit-efficient quantum image representation tailored for neutral-atom devices, by combining cartographic generalization with edge-based dot patterns embedded directly in Aquila's atomic layout. The approach avoids costly state preparation associated with digital quantum image processing and uses RDP-driven pruning to compress representations without fidelity loss, enabling megapixel-scale images to be processed with a small number of qubits. Through Bloqade simulations and Chamfer-distance matching, the authors demonstrate feasible image-matching tasks on as few as 9–21 atoms, highlighting potential for quantum-native machine learning pipelines and energy-efficient visual processing. The study positions SDR as a scalable, analog-qubit paradigm that could complement or replace classical AI pipelines for certain vision tasks, particularly in resource-constrained or energy-aware settings.
Abstract
In this work, we introduce a fundamentally new paradigm for quantum image representation tailored for neutral-atom quantum devices. The proposed method constructs a qubit-efficient image representation by first applying a cartographic generalization algorithm to a classical edge-extracted input image, yielding a highly optimized sparse-dot based geometric description. While ensuring the structural integrity of the image, this sparse representation is then embedded into the atomic configuration of Aquila (QuEra Computing Inc.), modeled through the Bloqade simulation software stack. By encoding visual information through physical atom placement rather than digital basis-state coding, the approach avoids the costly state-preparation overhead inherent to digital quantum image processing circuits. Additionally, pruning sparse dot images, akin to map feature reduction, compresses representations without fidelity loss, thereby substantially reducing qubit requirements when implemented on an analog neutral-atom quantum device. The resulting quantum-native images have been successfully evaluated through matching tasks against an image database, thus illustrating the feasibility of this approach for image matching applications. Since sparse-dot image representations enable seamless generation of synthetic datasets, this work constitutes an initial step towards fully quantum-native machine-learning pipelines for visual data and highlights the potential of scalable analog quantum computing to enable resource-efficient alternatives to energy-intensive classical AI-based image processing frameworks.