Analog Quantum Image Representation with Qubit-Frugal Encoding

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.

Summary

This paper introduces a novel approach to quantum image representation called "Sparse Dots Representation" (SDR) tailored for neutral-atom quantum devices like QuEra's Aquila. The core idea is to represent images using a sparse set of points corresponding to the edges of objects, which can then be directly mapped to the positions of neutral atoms in the quantum device. This avoids the state-preparation overhead associated with traditional digital quantum image processing (QImP) methods that encode pixel values in qubit states. The SDR method first performs classical edge extraction on the input image, followed by a cartographic generalization algorithm (Ramer-Douglas-Peucker) to reduce the number of points representing the edges. These sparse points are then directly mapped to the physical atomic array on the quantum device using Bloqade SDK. The resulting quantum-native image can then be used for image matching tasks, as demonstrated by using Chamfer distance to compare the SDR representation against a database of images. The key contribution of this work is a qubit-frugal analog quantum image representation that overcomes the limitations of existing QImP methods, which often require a large number of qubits scaling with the image resolution. By encoding visual information directly into the physical arrangement of atoms, SDR avoids the need for complex quantum circuits for state preparation and manipulation. The authors demonstrate the feasibility of this approach through simulations, showing that complex images can be represented using a relatively small number of atoms, making it suitable for near-term neutral-atom quantum computers. This approach is important because it paves the way for fully quantum-native machine learning pipelines for visual data and offers a potential resource-efficient alternative to energy-intensive classical AI-based image processing.

Key Insights

• •The SDR method leverages cartographic generalization (specifically the Ramer-Douglas-Peucker algorithm) to reduce the number of qubits required to represent an image, enabling the use of larger, more complex images on near-term quantum hardware.
• •SDR encodes visual information through physical atom placement in a neutral-atom quantum device, avoiding the state-preparation overhead inherent in digital QImP.
• •The paper demonstrates that complex images can be represented with a relatively small number of atoms (often between 9-21 atoms for industrial value items), making SDR significantly more qubit-efficient than existing QImR techniques (Table 1 indicates SDR qubit requirement is independent of image resolution 'n', unlike FRQI, QPIE, and NEQR).
• •SDR is designed to be directly compatible with analog neutral-atom hardware like QuEra's Aquila, enabling geometry-native quantum image processing that is unavailable to other QImR methods.
• •The use of Chamfer distance for image matching with SDR representations demonstrates the feasibility of using this approach for real-world applications.
• •The paper highlights the potential of analog quantum computing for energy-efficient AI, noting that QuEra's Aquila consumes significantly less power than classical supercomputers (less than 7kW compared to supercomputers).
• •A potential future direction involves using the Hamiltonian energy of atom arrangements as a matching criterion, which would leverage the native capabilities of analog quantum devices.

Practical Implications

• •SDR can be used in industrial automation tasks where image contours are sufficient for matching/classification, reducing the computational load on miniature drones and medical robots by offloading image understanding to a centralized hybrid quantum-classical computer.
• •This research opens the door for quantum-native machine learning pipelines for visual data, potentially leading to more efficient and scalable AI algorithms for image processing.
• •Practitioners and engineers can use SDR to represent images on near-term neutral-atom quantum computers for tasks such as image matching and object recognition, leveraging the unique capabilities of these devices.
• •Future research directions include exploring the use of SDR with quantum reservoir computing (QRC) for machine learning tasks and developing new algorithms that directly leverage the Hamiltonian energy of the atom arrangements for image processing.
• •The demonstrated energy efficiency of neutral-atom quantum computing suggests a path toward more sustainable AI, particularly for computationally intensive tasks like image processing.

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