Quantum Compressed Sensing Enables Image Classification with a Single Photon
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915963018936320 |
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| author | Fan, Yanshan Hu, Jianyong Wu, Shuxiao Qiao, Zhixing Feng, Guosheng Yang, Changgang Liu, Jianqiang Chen, Ruiyun Qin, Chengbing Zhang, Guofeng Xiao, Liantuan Jia, Suotang |
| author_facet | Fan, Yanshan Hu, Jianyong Wu, Shuxiao Qiao, Zhixing Feng, Guosheng Yang, Changgang Liu, Jianqiang Chen, Ruiyun Qin, Chengbing Zhang, Guofeng Xiao, Liantuan Jia, Suotang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25480 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Quantum Compressed Sensing Enables Image Classification with a Single Photon Fan, Yanshan Hu, Jianyong Wu, Shuxiao Qiao, Zhixing Feng, Guosheng Yang, Changgang Liu, Jianqiang Chen, Ruiyun Qin, Chengbing Zhang, Guofeng Xiao, Liantuan Jia, Suotang Quantum Physics Optics 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. |
| title | Quantum Compressed Sensing Enables Image Classification with a Single Photon |
| topic | Quantum Physics Optics |
| url | https://arxiv.org/abs/2604.25480 |