We develop a physics-based model for classical computation based on autonomous
quantum thermal machines. These machines consist of few interacting quantum bits (qubits) …

Linear algebra is central to many algorithms in engineering, science, and machine learning;
hence, accelerating it would have tremendous economic impact. Quantum computing has …

Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for
novel computing hardware in order to truly unlock the potential for AI. Physics-based …

A fully Bayesian treatment of complicated predictive models (such as deep neural networks)
would enable rigorous uncertainty quantification and the automation of higher-level tasks …

Second-order training methods have better convergence properties than gradient descent
but are rarely used in practice for large-scale training due to their computational overhead …

Thermodynamic computing has emerged as a promising paradigm for accelerating
computation by harnessing the thermalization properties of physical systems. This work …

While physics-based computing can offer speed and energy efficiency compared to digital
computing, it also is subject to errors that must be mitigated. For example, many error …