Hardware-inspired Continuous Variables Quantum Optical Neural Networks

• URL: http://arxiv.org/abs/2512.05204v1
• Date: Thu, 04 Dec 2025 19:20:27 GMT
• Title: Hardware-inspired Continuous Variables Quantum Optical Neural Networks
• Authors: Todor Krasimirov-Ivanov, Alba Cervera-Lierta, Paolo Stornati, Federico Centrone,
• Abstract summary: In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes.<n>This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks.
• Score: 0.27998963147546146
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes while non-Gaussian resources -- the challenging piece for physical implementation -- originate the nonlinear effects, unlocking quantum analogs of an artificial neuron. This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks (QONNs) developed with available photonic components: coherent states as input encoding, a general Gaussian transformation followed by multi-mode photon subtractions as the processing layer, and homodyne detection as outputs readout. The closed-form expressions of such architecture are derived demonstrating the family of adaptive activations and the quantum-optical neurons that emerge from the amount of photon-subtracted modes, proving that the proposed design satisfies the Universal Approximation Theorem within a single layer. To classically simulate the QONN training, the high-performance QuaNNTO library has been developed based on Wick--Isserlis expansion and Bogoliubov transformations, allowing multi-layer exact expectation values of non-Gaussian states without truncating the infinite-dimensional Hilbert space. Experiments on supervised learning and state-preparation tasks show balanced-resource efficiency with strong expressivity and generalization capabilities, illustrating the potential of the architecture for scalable photonic quantum machine learning and for quantum applications such as complex non-Gaussian gate synthesis.

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