Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Quantum computers have demonstrable ability to solve problems at a scale beyond brute-
force classical simulation. Interest in quantum algorithms has developed in many areas …

Developing scalable quantum algorithms to study finite-temperature physics of quantum
many-body systems has attracted considerable interest due to recent advancements in …

The phase diagram of strong interactions in nature at finite temperature and chemical
potential remains largely theoretically unexplored due to inadequacy of Monte-Carlo–based …

Recent advances in quantum computers are demonstrating the ability to solve problems at a
scale beyond brute force classical simulation. As such, a widespread interest in quantum …

Variational quantum algorithms (VQAs) are widely speculated to deliver quantum
advantages for practical problems under the quantum–classical hybrid computational …

The thermal or equilibrium ensemble is one of the most ubiquitous states of matter. For
models comprised of many locally interacting quantum particles, it describes a wide range of …

Quantum neural networks (QNNs) have been a promising framework in pursuing near-term
quantum advantage in various fields, where many applications can be viewed as learning a …

Abstract Weak values and Kirkwood–Dirac (KD) quasiprobability distributions have been
independently associated with both foundational issues in quantum theory and advantages …

Estimating the difference between quantum data is crucial in quantum computing. However,
as typical characterizations of quantum data similarity, the trace distance and quantum …