Machine Learning Quantum Systems with Magnetic p-bits
This addresses the hardware crisis for AI workloads by proposing a domain-specific, incremental approach to probabilistic computing.
The paper tackles the need for scalable and energy-efficient hardware for AI by exploring how a probabilistic computer using magnetic p-bits can be applied to machine learning in quantum systems, though no concrete results or numbers are provided.
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.