Modeling and benchmarking quantum optical neurons for efficient neural computation

• URL: http://arxiv.org/abs/2509.01784v1
• Date: Mon, 01 Sep 2025 21:30:47 GMT
• Title: Modeling and benchmarking quantum optical neurons for efficient neural computation
• Authors: Andrea Andrisani, Gennaro Vessio, Fabrizio Sgobba, Francesco Di Lena, Luigi Amato Santamaria, Giovanna Castellano,
• Abstract summary: Quantum optical neurons (QONs) are emerging as promising computational units that leverage photonic interference to perform neural operations.<n>We introduce a family of QON architectures based on Hong-Ou-Mandel (HOM) and Mach-Zehnder (MZ) interferometers, incorporating different photon modulation strategies.<n>We evaluate these QONs both in isolation and as building blocks of multilayer networks, training them on binary and multiclass image classification tasks.
• Score: 7.353780961304017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum optical neurons (QONs) are emerging as promising computational units that leverage photonic interference to perform neural operations in an energy-efficient and physically grounded manner. Building on recent theoretical proposals, we introduce a family of QON architectures based on Hong-Ou-Mandel (HOM) and Mach-Zehnder (MZ) interferometers, incorporating different photon modulation strategies -- phase, amplitude, and intensity. These physical setups yield distinct pre-activation functions, which we implement as fully differentiable modules in software. We evaluate these QONs both in isolation and as building blocks of multilayer networks, training them on binary and multiclass image classification tasks using the MNIST and FashionMNIST datasets. Our experiments show that two configurations -- HOM-based amplitude modulation and MZ-based phase-shifted modulation -- achieve performance comparable to that of classical neurons in several settings, and in some cases exhibit faster or more stable convergence. In contrast, intensity-based encodings display greater sensitivity to distributional shifts and training instabilities. These results highlight the potential of QONs as efficient and scalable components for future quantum-inspired neural architectures and hybrid photonic-electronic systems.

Related papers

• Hardware-inspired Continuous Variables Quantum Optical Neural Networks [0.27998963147546146]
  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.
  arXiv  Detail & Related papers  (2025-12-04T19:20:27Z)
• SEPhIA: <1 laser/neuron Spiking Electro-Photonic Integrated Multi-Tiled Architecture for Scalable Optical Neuromorphic Computing [0.11971027459760782]
  We introduce SEPhIA, a photonic-electronic, multi-tiled SNN architecture emphasizing implementation feasibility and realistic scaling.<n> SEPhIA leverages microring resonator modulators (MRMs) and multi-wavelength sources to achieve effective sub-one-laser-per-spiking neuron efficiency.
  arXiv  Detail & Related papers  (2025-10-08T18:24:29Z)
• Enhanced image classification via hybridizing quantum dynamics with classical neural networks [0.0]
  We present a hybrid protocol which combines classical neural networks with non-equilibrium dynamics of a quantum many-body system for image classification.<n>This architecture leverages classical neural networks to efficiently process high-dimensional data and encode it effectively on a quantum many-body system.
  arXiv  Detail & Related papers  (2025-07-18T00:15:14Z)
• Multi-Photon Quantum Rabi Models with Center-of-Mass Motion [45.73541813564926]
  We introduce a rigorous, second-quantized framework for describing multi-$Lambda$-atoms in a cavity.<n>A key feature of our approach is the systematic application of a Hamiltonian averaging theory to the atomic field operators.<n>A significant finding is the emergence of a particle-particle interaction mediated by ancillary states.
  arXiv  Detail & Related papers  (2025-07-07T09:50:48Z)
• Neural Quantum Digital Twins for Optimizing Quantum Annealing [2.8579459256051316]
  We propose a Neural Quantum Digital Twin (NQDT) framework that reconstructs the energy landscape of quantum many-body systems.<n>NQDT accurately captures key quantum phenomena, including quantum criticality and phase transitions.
  arXiv  Detail & Related papers  (2025-05-21T15:38:55Z)
• Photonic Quantum Convolutional Neural Networks with Adaptive State Injection [0.39928148142956393]
  We design and experimentally implement the first photonic quantum convolutional neural network (PQCNN) based on particle-number preserving circuits equipped with state injection.<n>We experimentally validate the PQCNN for a binary image classification on a photonic platform utilizing a semiconductor quantum dot-based single-photon source.<n>We highlight the potential utility of a simple adaptive technique for a nonlinear Boson Sampling task, compatible with near-term quantum devices.
  arXiv  Detail & Related papers  (2025-04-29T17:57:01Z)
• Quantum Generative Models for Image Generation: Insights from MNIST and MedMNIST [0.0]
  We introduce two novel noise strategies: intrinsic quantum-generated noise and a tailored noise scheduling mechanism.<n>We evaluate our model on MNIST and MedMNIST datasets to examine its feasibility and performance.
  arXiv  Detail & Related papers  (2025-03-30T06:36:22Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Quantum Electrodynamics Coupled-Cluster Theory: Exploring Photon-Induced Electron Correlations [0.1666604949258699]
  We present our successful implementation of the quantum electrodynamics coupled-cluster method with single and double excitations. We discuss theoretical foundations, algorithmic details, and numerical benchmarks to demonstrate how the integration of bosonic degrees of freedom alters the electronic ground state.
  arXiv  Detail & Related papers  (2024-09-10T20:48:47Z)
• Towards Efficient Quantum Hybrid Diffusion Models [68.43405413443175]
  We propose a new methodology to design quantum hybrid diffusion models. We propose two possible hybridization schemes combining quantum computing's superior generalization with classical networks' modularity.
  arXiv  Detail & Related papers  (2024-02-25T16:57:51Z)
• Quantum machine learning for image classification [39.58317527488534]
  This research introduces two quantum machine learning models that leverage the principles of quantum mechanics for effective computations. Our first model, a hybrid quantum neural network with parallel quantum circuits, enables the execution of computations even in the noisy intermediate-scale quantum era. A second model introduces a hybrid quantum neural network with a Quanvolutional layer, reducing image resolution via a convolution process.
  arXiv  Detail & Related papers  (2023-04-18T18:23:20Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Tunable photon-mediated interactions between spin-1 systems [68.8204255655161]
  We show how to harness multi-level emitters with several optical transitions to engineer photon-mediated interactions between effective spin-1 systems. Our results expand the quantum simulation toolbox available in cavity QED and quantum nanophotonic setups.
  arXiv  Detail & Related papers  (2022-06-03T14:52:34Z)
• Quantum Markov Chain Monte Carlo with Digital Dissipative Dynamics on Quantum Computers [52.77024349608834]
  We develop a digital quantum algorithm that simulates interaction with an environment using a small number of ancilla qubits. We evaluate the algorithm by simulating thermal states of the transverse Ising model.
  arXiv  Detail & Related papers  (2021-03-04T18:21:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.