Global warming is one of the most compelling environmental threats today, as the rise in
energy consumption and CO2 emission caused a dreadful impact on our environment. The …

Modern generative machine learning models are able to create realistic outputs far beyond
their training data, such as photorealistic artwork, accurate protein structures or …

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 …

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 …

Many Artificial Intelligence (AI) algorithms are inspired by physics and employ stochastic
fluctuations. We connect these physics-inspired AI algorithms by unifying them under a …

Thermodynamic computing exploits fluctuations and dissipation in physical systems to
efficiently solve various mathematical problems. It was recently shown that certain linear …

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 …

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 …

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 …