aiqbench analysis
This research proposes a hybrid training framework that replaces standard dissipative relaxation with extended phase-space dynamics to mitigate local minima and accelerate convergence in energy-based models. By leveraging Ising-machine-inspired physics, the method achieves noise robustness and performance comparable to backpropagation on standard benchmarks while addressing the high energy costs of conventional GPU training. The approach represents a significant step toward physically efficient learning schemes that maintain deep network capabilities without relying solely on traditional optimization routes.
Hybridizing Equilibrium Propagation with Ising Machines for Efficient Energy-Based Learning
arXiv:2606.09112v1 Announce Type: new Abstract: The rapid evolution of artificial intelligence has led to substantial advances in deep neural networks. Nonetheless, conventional GPU-based training remains highly energy-demanding, motivating the exploration of physical dynamics and compatible energy-based learning schemes, such as equilibrium propagation (EP). EP-based training, however, frequently suffers from convergence to local minima due to phase-space contraction. Here we introduce an Ising-dynamics-inspired equilibrium-propagation framework in which dissipative Hopfield relaxation is replaced by an extended phase-space dynamics with conjugate variables. The resulting training paradigm keeps the local two-phase learning rule of EP while changing the physical route by which neural states reach equilibrium. We show that this dynamics lowers effective energy barriers, accelerates convergence, improves noise robustness, and trains deep convolutional Hopfield networks on MNIST, FashionMNIST, and CIFAR-10 with performance comparable to backpropagation.
Abstract:The rapid evolution of artificial intelligence has led to substantial advances in deep neural networks. Nonetheless, conventional GPU-based training remains highly energy-demanding, motivating the exploration of physical dynamics and compatible energy-based learning schemes, such as equilibrium propagation (EP). EP-based training, however, frequently suffers from convergence to local minima due to phase-space contraction. Here we introduce an Ising-dynamics-inspired equilibrium-propagation framework in which dissipative Hopfield relaxation is replaced by an extended phase-space dynamics with conjugate variables. The resulting training paradigm keeps the local two-phase learning rule of EP while changing the physical route by which neural states reach equilibrium. We show that this dynamics lowers effective energy barriers, accelerates convergence, improves noise robustness, and trains deep convolutional Hopfield networks on MNIST, FashionMNIST, and CIFAR-10 with performance comparable to backpropagation.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.09112 [cs.LG]
(or arXiv:2606.09112v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.09112 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 224 words
Related coverage
Optimizing Energy-based Neural Network Training with Coherent Ising Machine
arXiv · Jun 9, 2026
EPIC-CIM: Training Convolutional Neural Networks on a Coherent Ising Machine via Equilibrium Propagation
arXiv · Jul 21, 2026
D - Wave Said Classical Computers Could Not Match Its Quantum Chip . A Laptop Just Did .
techtimes.com · Jul 21, 2026
News
Quantum Computing Report · Jun 7, 2026