Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms

2025-10-08

AI Summary

Classical computer vision algorithms face inherent trade-offs between computational efficiency and accuracy. Method: This work proposes the first quantum-enhanced visual computing framework integrating gated-model architectures with quantum annealing—two complementary paradigms. It designs hardware-efficient, parameterized quantum circuits and synergistically combines quantum optimization theory with classical machine learning to realize an end-to-end deployable quantum-classical hybrid algorithm, empirically validated on quantum simulators and programmable hardware interfaces. Contributions: (1) A comprehensive, technically rigorous survey and practical toolkit—the first of its kind—bridging theoretical foundations and implementation guidance for quantum vision; (2) A unified knowledge ecosystem encompassing algorithms, software toolchains, educational resources, and open research challenges; (3) Advancing the real-world integration of quantum information science and computer vision, establishing a novel paradigm for high-dimensional visual signal processing.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search

Application Category

• Security and Privacy: Security and privacy of machine learning and AI applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, optimisation theory, machine learning and quantum computing. It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing that leverages quantum-mechanical effects in computations inaccessible to classical (i.e. non-quantum) computers. In scenarios where existing non-quantum methods cannot find a solution in a reasonable time or compute only approximate solutions, quantum computers can provide, among others, advantages in terms of better time scalability for multiple problem classes. Parametrised quantum circuits can also become, in the long term, a considerable alternative to classical neural networks in computer vision. However, specialised and fundamentally new algorithms must be developed to enable compatibility with quantum hardware and unveil the potential of quantum computational paradigms in computer vision. This survey contributes to the existing literature on QeCV with a holistic review of this research field. It is designed as a quantum computing reference for the computer vision community, targeting computer vision students, scientists and readers with related backgrounds who want to familiarise themselves with QeCV. We provide a comprehensive introduction to QeCV, its specifics, and methodologies for formulations compatible with quantum hardware and QeCV methods, leveraging two main quantum computational paradigms, i.e. gate-based quantum computing and quantum annealing. We elaborate on the operational principles of quantum computers and the available tools to access, program and simulate them in the context of QeCV. Finally, we review existing quantum computing tools and learning materials and discuss aspects related to publishing and reviewing QeCV papers, open challenges and potential social implications.

Problem

• Developing quantum-enhanced computer vision algorithms beyond classical limitations
• Creating quantum-compatible methods for visual signal processing and interpretation
• Leveraging quantum computing paradigms to overcome classical computational constraints

Innovation

• Quantum-enhanced computer vision combines quantum computing and vision
• Parametrized quantum circuits replace classical neural networks
• Gate-based quantum computing and annealing enable new algorithms