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) …

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 …

Linear algebraic primitives are at the core of many modern algorithms in engineering,
science, and machine learning. Hence, accelerating these primitives with novel computing …

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 …

Maxwellian ratchets are autonomous, finite-state thermodynamic engines that implement
input-output informational transformations. Previous studies of these “demons” focused on …

Nonequilibrium information thermodynamics determines the minimum energy dissipation to
reliably erase memory under time-symmetric control protocols. We demonstrate that its …

We introduce a fast and highly efficient physically realizable bit swap. Employing readily
available and scalable Josephson junction microtechnology, the design implements the …