Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine

• URL: http://arxiv.org/abs/2509.13917v1
• Date: Wed, 17 Sep 2025 11:23:24 GMT
• Title: Global Mean-Amplitude Enhanced Spiking Neural Network Coherent Ising Machine
• Authors: Yan Chen Jiang, Lu Ma, Chuan Wang, Tie Jun Wang,
• Abstract summary: A new global mean-amplitude feedback-enhanced spiking neural network CIM (GFSNN-CIM) is introduced.<n>The GFSNN-CIM achieves up to a 27% improvement in solution success rates compared to conventional spiking neural network CIM.
• Score: 6.142643738867997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The coherent Ising machine (CIM) is a quantum-inspired computing platform that leverages optical parametric oscillation dynamics to solve combinatorial optimization problems by searching for the ground state of an Ising Hamiltonian. Conventional CIM implementations face challenges in handling non-uniform coupling strengths and maintaining amplitude stability during computation. In this paper, a new global mean-amplitude feedback-enhanced spiking neural network CIM (GFSNN-CIM) is introduced with a physics-driven amplitude stabilization mechanism to dynamically balance nonlinear gain saturation and coupling effects. This modification enhances synchronization in the optical pulse network, leading to more robust convergence under varying interaction strengths. Experimental validation on Max-Cut problems demonstrates that the GFSNN-CIM achieves up to a 27% improvement in solution success rates compared to conventional spiking neural network CIM, with scalability improving as problem complexity increases. Further application to the traffic assignment problem (TAP) confirms the method's generality; the GFSNN-CIM achieves near-continuous accuracy (deviations < 0.035%) even at coarse discretization, while large-scale tests on Beijing's road network (481 spins) validate its real-world applicability. These advances establish a physics-consistent optimization framework, where optical pulse dynamics directly encode combinatorial problems, paving the way for scalable, high-performance CIM implementations in complex optimization tasks.

Related papers

• PhyG-MoE: A Physics-Guided Mixture-of-Experts Framework for Energy-Efficient GNSS Interference Recognition [49.955269674859004]
  This paper introduces PhyG-MoE (Physics-Guided Mixture-of-Experts), a framework designed to align model capacity with signal complexity.<n>Unlike static architectures, the proposed system employs a spectrum-based gating mechanism that routes signals based on their spectral feature entanglement.<n>A high-capacity TransNeXt expert is activated on-demand to disentangle complex features in saturated scenarios, while lightweight experts handle fundamental signals to minimize latency.
  arXiv  Detail & Related papers  (2026-01-19T07:57:52Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• MAD-NG: Meta-Auto-Decoder Neural Galerkin Method for Solving Parametric Partial Differential Equations [5.767740428776141]
  Parametric partial differential equations (PDEs) are fundamental for modeling a wide range of physical and engineering systems.<n>Traditional neural network-based solvers, such as Physics-Informed Neural Networks (PINNs) and Deep Galerkin Methods, often face challenges in generalization and long-time prediction efficiency.<n>We propose a novel and scalable framework that significantly enhances the Neural Galerkin Method (NGM) by incorporating the Meta-Auto-Decoder (MAD) paradigm.
  arXiv  Detail & Related papers  (2025-12-25T11:27:40Z)
• DBAW-PIKAN: Dynamic Balance Adaptive Weight Kolmogorov-Arnold Neural Network for Solving Partial Differential Equations [11.087203453701568]
  Physics-informed neural networks (PINNs) have led to significant advancements in scientific computing.<n> PINNs encounter persistent and severe challenges related to stiffness in gradient flow and spectral bias.<n>This paper proposes a Dynamic Balancing Adaptive Weighting Physics-Informed Kolmogorov-Arnold Network (DBAW-PIKAN)
  arXiv  Detail & Related papers  (2025-12-25T06:47:14Z)
• TensoMeta-VQC: A Tensor-Train-Guided Meta-Learning Framework for Robust and Scalable Variational Quantum Computing [60.996803677584424]
  TensoMeta-VQC is a novel tensor-train (TT)-guided meta-learning framework designed to improve the robustness and scalability of VQC significantly.<n>Our framework fully delegates the generation of quantum circuit parameters to a classical TT network, effectively decoupling optimization from quantum hardware.
  arXiv  Detail & Related papers  (2025-08-01T23:37:55Z)
• Time Marching Neural Operator FE Coupling: AI Accelerated Physics Modeling [3.0635300721402228]
  This work introduces a novel hybrid framework that integrates physics-informed deep operator network with FEM through domain decomposition.<n>To address the challenges of dynamic systems, we embed a time stepping scheme directly into the DeepONet, substantially reducing long-term error propagation.<n>Our framework shows accelerated convergence rates (up to 20% improvement in convergence rates compared to conventional FE coupling approaches) while preserving solution fidelity with error margins consistently below 3%.
  arXiv  Detail & Related papers  (2025-04-15T16:54:04Z)
• Enhancing Physics-Informed Neural Networks with a Hybrid Parallel Kolmogorov-Arnold and MLP Architecture [0.0]
  We propose a novel architecture that integrates parallelized KAN and branches within a unified PINN framework.<n>The HPKM-PINN introduces a scaling factor xi, to optimally balance the complementary strengths of KAN's interpretable function approximation and numerical's nonlinear learning.<n>These findings highlight the HPKM-PINN's ability to leverage KAN's interpretability and robustness, positioning it as a versatile and scalable tool for solving complex PDE-driven problems.
  arXiv  Detail & Related papers  (2025-03-30T02:59:32Z)
• Deep-Unrolling Multidimensional Harmonic Retrieval Algorithms on Neuromorphic Hardware [78.17783007774295]
  This paper explores the potential of conversion-based neuromorphic algorithms for highly accurate and energy-efficient single-snapshot multidimensional harmonic retrieval.<n>A novel method for converting the complex-valued convolutional layers and activations into spiking neural networks (SNNs) is developed.<n>The converted SNNs achieve almost five-fold power efficiency at moderate performance loss compared to the original CNNs.
  arXiv  Detail & Related papers  (2024-12-05T09:41:33Z)
• MG-Net: Learn to Customize QAOA with Circuit Depth Awareness [51.78425545377329]
  Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling optimization challenges. The requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. We introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians.
  arXiv  Detail & Related papers  (2024-09-27T12:28:18Z)
• Encoding arbitrary Ising Hamiltonians on Spatial Photonic Ising Machines [0.0]
  We introduce and experimentally validate a SPIM instance that enables direct control over the full interaction matrix. We demonstrate the conformity of the experimentally measured Ising energy with the theoretically expected values and then proceed to solve both the unweighted and weighted graph problems. Our approach greatly expands the applicability of SPIMs for real-world applications without sacrificing any of the inherent advantages of the system.
  arXiv  Detail & Related papers  (2024-07-12T10:54:07Z)
• Accelerating Continuous Variable Coherent Ising Machines via Momentum [16.545815849819043]
  We propose to modify CV-CIM dynamics using more tunable optimization techniques such as momentum and Adam. We show that momentum and Adam-CIM's and sample Adam-CV-CIM's performance is more stable as an tunable framework.
  arXiv  Detail & Related papers  (2024-01-22T17:18:53Z)
• Pointer Networks with Q-Learning for Combinatorial Optimization [55.2480439325792]
  We introduce the Pointer Q-Network (PQN), a hybrid neural architecture that integrates model-free Q-value policy approximation with Pointer Networks (Ptr-Nets) Our empirical results demonstrate the efficacy of this approach, also testing the model in unstable environments.
  arXiv  Detail & Related papers  (2023-11-05T12:03:58Z)
• Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks [51.92362217307946]
  Physics-informed neural networks (PINNs) have effectively been demonstrated in solving forward and inverse differential equation problems. PINNs are trapped in training failures when the target functions to be approximated exhibit high-frequency or multi-scale features. In this paper, we propose to employ implicit gradient descent (ISGD) method to train PINNs for improving the stability of training process.
  arXiv  Detail & Related papers  (2023-03-03T08:17:47Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)

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.