Machine Learning Quantum Systems with Magnetic p-bits

• URL: http://arxiv.org/abs/2310.06679v1
• Date: Tue, 10 Oct 2023 14:54:57 GMT
• Title: Machine Learning Quantum Systems with Magnetic p-bits
• Authors: Shuvro Chowdhury and Kerem Y. Camsari
• Abstract summary: 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. Probability computing with p-bits emerged as a scalable, domain-specific, and energy-efficient computing paradigm.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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