# Correspondence Between Ising Machines and Neural Networks

arXiv: https://arxiv.org/abs/2511.00746 · HTML: https://mlsanity.com/paper/2511.00746/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 2 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Ising formulations of many NP problems](https://mlsanity.com/paper/1302.5843/) (2013)
- [Inverse statistical problems: from the inverse Ising problem to data science](https://mlsanity.com/paper/1702.01522/) (2017)
- [Training an Ising Machine with Equilibrium Propagation](https://mlsanity.com/paper/2305.18321/) (2023)
- [Provable Post-Training Quantization: Theoretical Analysis of OPTQ and Qronos](https://mlsanity.com/paper/2508.04853/) (2025)
- [Uncovering a Universal Abstract Algorithm for Modular Addition in Neural Networks](https://mlsanity.com/paper/2505.18266/) (2025)
- [Solving the compute crisis with physics-based ASICs](https://mlsanity.com/paper/2507.10463/) (2025)
- [An efficient probabilistic hardware architecture for diffusion-like models](https://mlsanity.com/paper/2510.23972/) (2025)
- [Clustering and Alignment: Understanding the Training Dynamics in Modular Addition](https://mlsanity.com/paper/2408.09414/) (2024)
- [Training Multi-layer Neural Networks on Ising Machine](https://mlsanity.com/paper/2311.03408/) (2023)
- [Design of General Purpose Minimal-Auxiliary Ising Machines](https://mlsanity.com/paper/2310.16246/) (2023)
- [AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators](https://mlsanity.com/paper/2411.01008/) (2024)

## Built on by (most cited first, 1 of 1)

- [Scaling Up Thermodynamic AI Models](https://mlsanity.com/paper/2607.00170/) (2026)

## Abstract (arXiv)

Computation with the Ising model is central to future computing technologies like quantum annealing, adiabatic quantum computing, and thermodynamic classical computing. Traditionally, computed values have been equated with ground states. This paper generalizes computation with ground states to computation with spin averages, allowing computations to take place at high temperatures. It then introduces a systematic correspondence between Ising devices and neural networks and a simple method to run trained feed-forward neural networks on Ising-type hardware. Finally, a mathematical proof is offered that these implementations are always successful.