Machine Learning Quantum Systems with Magnetic p-bits
Shuvro Chowdhury Kerem Y. Camsari
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.