Qubit-efficient Variational Quantum Algorithms for Image Segmentation

TL;DR

This paper explores qubit-efficient variational quantum algorithms for image segmentation, formulating it as a graph cut problem, and introduces a new adaptive cost encoding method that improves training convergence and reduces quantum resource requirements.

Contribution

It introduces Adaptive Cost Encoding (ACE), a novel approach that enhances quantum image segmentation by reducing qubit usage and improving convergence over existing methods.

Findings

ACE converges faster than PGE and ABE.

Quantum resources scale logarithmically with image size.

ACE demonstrates strengths and limitations in quantum image segmentation.

Abstract

Quantum computing is expected to transform a range of computational tasks beyond the reach of classical algorithms. In this work, we examine the application of variational quantum algorithms (VQAs) for unsupervised image segmentation to partition images into separate semantic regions. Specifically, we formulate the task as a graph cut optimization problem and employ two established qubit-efficient VQAs, which we refer to as Parametric Gate Encoding (PGE) and Ancilla Basis Encoding (ABE), to find the optimal segmentation mask. In addition, we propose Adaptive Cost Encoding (ACE), a new approach that leverages the same circuit architecture as ABE but adopts a problem-dependent cost function. We benchmark PGE, ABE and ACE on synthetically generated images, focusing on quality and trainability. ACE shows consistently faster convergence in training the parameterized quantum circuits in comparison to PGE and ABE. Furthermore, we provide a theoretical analysis of the scalability of these approaches against the Quantum Approximate Optimization Algorithm (QAOA), showing a significant cutback in the quantum resources, especially in the number of qubits that logarithmically depends on the number of pixels. The results validate the strengths of ACE, while concurrently highlighting its inherent limitations and challenges. This paves way for further research in quantum-enhanced computer vision.