Correspondence Between Ising Machines and Neural Networks
This work addresses the challenge of running neural networks on emerging computing hardware like quantum annealers, offering a foundational link between computational models.
The paper tackles the problem of generalizing Ising model computation from ground states to spin averages, enabling high-temperature operations, and establishes a systematic correspondence between Ising devices and neural networks, with a proof of successful implementation.
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.