Machine Learning Quantum Systems with Magnetic p-bits

TL;DR

This paper explores the use of magnetic p-bits, based on spintronic devices, for scalable probabilistic computing in machine learning and quantum physics applications, addressing energy efficiency and scalability challenges.

Contribution

It introduces magnetic p-bits as a promising hardware platform for probabilistic computing in machine learning and quantum systems, highlighting their potential advantages.

Findings

Magnetic p-bits can be integrated into scalable probabilistic computers.

Probabilistic computing with p-bits is energy-efficient for AI workloads.

Potential applications in machine learning and quantum physics are demonstrated.

Abstract

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.