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.

The nexus of traditional high performance computing and artificial intelligence is a fact, not a theory, and the exascale-class machinery installed in the United States, Europe, China, and Japan will be a showcase for how these two powerful simulation and analytical prediction techniques can be brought together in many different ways. A year ago, we wrote about some benchmarks done in China with the Tianhe-3 exascale prototype supercomputer running on custom native many-core Armv8-based Phytium 2000 processors. Now comes yet another research paper from more than a dozen scientists from multiple universities in China laying out a hybrid AI-HPC framework on the next-generation exascale Sunway system, the follow-on to the Sunway "TaihuLight" supercomputer that now sits at number four on the Top500 list of the world's fastest systems, combined with innovative neural network designs and deep learning principles to enable researchers to solve massive and highly complex problems. This effort referred to above is distinct from the BaGuaLu machine learning model that we covered back in March, which spanned 37.44 million cores and that juggled 14.5 trillion parameters. In this new AI-HPC mashup run on OceanLight, the challenge was what is called quantum many-body problems, which occur when large numbers of microscopic particles interact with each other, creating a quantum entanglement and resulting in a range of physical phenomena.

The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the exponential complexity of the many-body wave function. Here we demonstrate that systematic machine learning of the wave function can reduce this complexity to a tractable computational form for some notable cases of physical interest. We introduce a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons. A reinforcement-learning scheme we demonstrate is capable of both finding the ground state and describing the unitary time evolution of complex interacting quantum systems. Our approach achieves high accuracy in describing prototypical interacting spins models in one and two dimensions.

Elucidating the behavior of quantum interacting systems of many particles remains one of the biggest challenges in physics. Traditional numerical methods often work well, but some of the most interesting problems leave them stumped. Carleo and Troyer harnessed the power of machine learning to develop a variational approach to the quantum many-body problem (see the Perspective by Hush). The method performed at least as well as state-of-the-art approaches, setting a benchmark for a prototypical two-dimensional problem. With further development, it may well prove a valuable piece in the quantum toolbox.