Analog Quantum Image Representation with Qubit-Frugal Encoding

• URL: http://arxiv.org/abs/2512.18451v1
• Date: Sat, 20 Dec 2025 17:50:42 GMT
• Title: Analog Quantum Image Representation with Qubit-Frugal Encoding
• Authors: Vikrant Sharma, Neel Kanth Kundu,
• Abstract summary: We introduce a fundamentally new paradigm for quantum image representation tailored for neutral-atom quantum devices.<n>The proposed method constructs a qubit-efficient image representation by first applying a cartographic generalization algorithm to a classical edge-extracted input image.<n>While ensuring the structural integrity of the image, this sparse representation is then embedded into the atomic configuration of Aquila.
• Score: 4.372268810108838
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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