Quantum Compressed Sensing Enables Image Classification with a Single Photon

• URL: http://arxiv.org/abs/2604.25480v1
• Date: Tue, 28 Apr 2026 10:33:20 GMT
• Title: Quantum Compressed Sensing Enables Image Classification with a Single Photon
• Abstract summary: We develop a photon level image classification method using quantum compressed sensing.<n>By exploiting the parallelism of photonic quantum superposition states, a single photon can be encoded the complete spatial information of a high-dimensional image.<n>It provides a foundation for intelligent sensing systems that operate under extreme photon budgets and harsh environments.
• Score: 2.5112823693334163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image classification is a core task of intelligent sensing, conventionally follows a sequential imaging then processing pipeline. However, redundant high-dimensional image reconstruction is inherently inefficient, especially in photon limited scenarios. Here we report a photon level image classification method using quantum compressed sensing, which reformulates the classification task as a sparse signal measurement problem directly oriented toward class labels. By exploiting the parallelism of photonic quantum superposition states, a single photon can be encoded the complete spatial information of a high-dimensional image. Through a diffractive deep neural network, we physically construct a dedicated measurement basis aligned with the class space, enabling signal-dependent adaptive compressive measurement. Ideally, our method can extract class information via a single quantum projective measurement, reducing the required number of measurements from the logarithmic scaling O(Klog(N/K)) of classical compressed sensing to the constant-order information-theoretic limit M = K = 1. Experimental results show that a classification accuracy of 69.0% can be achieved by using a single-photon detection event as the decision criterion, while it increases to 95.0% with four-photon detection events. This work demonstrates image classification at the energy efficiency limit and introduces a measurement as decision framework. It provides a foundation for intelligent sensing systems that operate under extreme photon budgets and harsh environments.

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