Resource-Efficient Quantum-Enhanced Compressive Imaging via Quantum Classical co-Design

TL;DR

This paper presents a co-design approach combining quantum hardware and classical compressive imaging techniques to significantly reduce quantum resource requirements while maintaining high imaging quality.

Contribution

It introduces a joint quantum-classical co-design framework that selectively applies squeezing based on principal component analysis to optimize resource use.

Findings

Achieves high-accuracy image classification with fewer squeezed modes.

Demonstrates high-fidelity image reconstruction with reduced quantum resources.

Shows that integrating quantum and classical methods enhances efficiency.

Abstract

Quantum sensing can enhance imaging performance by reducing measurement noise below the classical limit, thereby improving the signal-to-noise ratio (SNR) of acquired data. In conventional quantum imaging schemes, squeezing is applied independently to each pixel or spatial mode, leading to a quantum resource cost that scales linearly with image dimension. This approach implicitly separates quantum enhancement from classical post-processing, treating them as independent layers. In this work, we demonstrate that integrating quantum resource allocation with the guidance from classical compressive imaging, via co-design between the quantum hardware layer and the classical software layer, substantially reduces the required quantum resources. We employ principal component analysis (PCA) to identify a low-dimensional principal component subspace for measurement and apply squeezing selectively to the most informative spatial modes corresponding to these principal components. Our numerical experiments show that high-accuracy image classification and high-fidelity image reconstruction can be achieved with significantly fewer squeezed modes compared to pixel-wise squeezing. Our results establish a joint quantum classical co-design framework for resource-efficient quantum-enhanced imaging.