# Machine Learning Quantum Systems with Magnetic p-bits

arXiv: https://arxiv.org/abs/2310.06679 · HTML: https://mlsanity.com/paper/2310.06679/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 14 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Massively Parallel Probabilistic Computing with Sparse Ising Machines](https://mlsanity.com/paper/2110.02481/) (2021)
- [Training Deep Boltzmann Networks with Sparse Ising Machines](https://mlsanity.com/paper/2303.10728/) (2023)
- [CMOS-compatible Ising and Potts Annealing Using Single Photon Avalanche Diodes](https://mlsanity.com/paper/2211.12607/) (2022)

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

- [Generalized Probabilistic Approximate Optimization Algorithm](https://mlsanity.com/paper/2507.07420/) (2025)

## Abstract (arXiv)

The slowing down of Moore's Law has led to a crisis as the computing workloads of Artificial Intelligence (AI) algorithms continue skyrocketing. There is an urgent need for scalable and energy-efficient hardware catering to the unique requirements of AI algorithms and applications. In this environment, probabilistic computing with p-bits emerged as a scalable, domain-specific, and energy-efficient computing paradigm, particularly useful for probabilistic applications and algorithms. In particular, spintronic devices such as stochastic magnetic tunnel junctions (sMTJ) show great promise in designing integrated p-computers. Here, we examine how a scalable probabilistic computer with such magnetic p-bits can be useful for an emerging field combining machine learning and quantum physics.