Physical-Layer Machine Learning with Multimode Interferometric Photon Counting

• URL: http://arxiv.org/abs/2506.12309v1
• Date: Sat, 14 Jun 2025 02:10:19 GMT
• Title: Physical-Layer Machine Learning with Multimode Interferometric Photon Counting
• Authors: Jia-Jin Feng, Anthony J. Brady, Quntao Zhuang,
• Abstract summary: We propose a unified protocol that combines machine learning with interferometric photon counting to reduce noise and reveal correlations.<n>Our results show that multimode interferometric photon counting outperforms conventional homodyne detection proposed in prior works.
• Score: 0.40964539027092906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The learning of the physical world relies on sensing and data post-processing. When the signals are weak, multi-dimensional and correlated, the performance of learning is often bottlenecked by the quality of sensors, calling for integrating quantum sensing into the learning of such physical-layer data. An example of such a learning scenario is the stochastic quadrature displacements of electromagnetic fields, modeling optomechanical force sensing, radiofrequency photonic sensing, microwave cavity weak signal sensing and other applications. We propose a unified protocol that combines machine learning with interferometric photon counting to reduce noise and reveal correlations. By applying variational quantum learning with multimode programmable quantum measurements, we enhance signal extraction. Our results show that multimode interferometric photon counting outperforms conventional homodyne detection proposed in prior works for tasks like principal component analysis (PCA) and cross-correlation analysis (CCA), even below vacuum noise levels. To further enhance the performance, We also integrate entanglement-enhanced modules, in the form of squeezed state distribution and anti-squeezing at detection, into the protocol. Combining the multimode interferometric photon counting and multipartite entanglement, the proposed protocol provides a powerful toolbox for learning weak signals.

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