Miniaturization has been the driving force in contemporary technologies. However, two main
obstacles limit further progress: additional reduction in size has reached its quantum limit …

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

Thermodynamic computing exploits fluctuations and dissipation in physical systems to
efficiently solve various mathematical problems. For example, it was recently shown that …

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 …

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

Navigating the intricacies of thermal management at the quantum scale is a challenge in the
pursuit of advanced nanoscale technologies. To this extent, theoretical frameworks …

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 …

Extracting work from a physical system is one of the cornerstones of quantum
thermodynamics. The extractable work, as quantified by ergotropy, necessitates a complete …