Machine learning is becoming an important tool in condensed matter physics, opening new avenues for the study of complex quantum and/or many-body systems. As data-driven science emerges as a new paradigm of scientific research, this volume brings together leading experts to examine the role of machine learning across a broad range of problems in the field. Beyond presenting recent advances, the book discusses common opportunities and challenges that cut across diverse applications, including trainability, interpretability, inverse problems, and the search for new physical insights.

The chapters cover a broad spectrum of topics, including quantum phase recognition and phase transitions, quantum many-body problems, quantum chemistry, generative models, neural quantum states, self-energy spectroscopy, materials informatics, density functional theory, self-learning Monte Carlo methods, and quantum machine learning. Each contribution reviews the current state of the art while discussing the significance, challenges, and future directions of machine learning in its respective area.

Serving as both an introduction and a reference, this book is of interest to graduate students and researchers in condensed matter physics, materials science, computational physics, and quantum information science. By connecting research directions that have often evolved independently, it offers a coherent perspective on the relationship between machine learning and the physics of quantum matter.

Yusuke Nomura received his Ph.D. from the Department of Applied Physics at the University of Tokyo, Japan, in 2015. His doctoral research on first-principles studies of superconductivity in alkali-doped fullerides received the Dean’s Award and was later published as a volume in the Springer Theses series. Following a postdoctoral fellowship at École Polytechnique, France, he joined the University of Tokyo as a research associate in 2016. He was a research scientist at the RIKEN Center for Emergent Matter Science from 2019 to 2022 and an associate professor at Keio University from 2022 to 2024. Since 2024, he has been a professor at the Institute for Materials Research, Tohoku University, and since 2025 has also served as a principal investigator at the Advanced Institute for Materials Research. His research interests include computational condensed matter physics, strongly correlated electron systems, high-temperature superconductivity, and machine-learning approaches to quantum many-body problems. His work focuses on the development and application of first-principles, quantum many-body, and machine-learning methods for understanding and predicting the properties of quantum materials.

Masatoshi Imada received his Ph.D. in Physics from the University of Tokyo, Japan, in 1981. He began his academic career as a research associate at the Institute for Solid State Physics (ISSP), the University of Tokyo, and was a visiting researcher at the University of California from 1984 to 1985. In 1986, he joined Saitama University as a lecturer and an associate professor. He returned to ISSP as an associate professor in 1990 and became a professor in 1997. In 2006, he moved to the Department of Applied Physics at the University of Tokyo as a professor until his retirement in 2019. Since then, he has continued his research as a fellow at the Toyota Physical and Chemical Research Institute, a research professor at Waseda University, a visiting professor at Sophia University and a professor emeritus at the University of Tokyo. Through his research life, he has been studying theory of condensed matter physics, statistical physics and computational physics in various areas. He has founded Multi-scale Ab initio Scheme for Correlated Electrons (MACE) for real quantum matter, and applied it to a wide range of strongly correlated electron materials, including copper-oxide and iron-based superconductors as well as molecular solids to identify mechanisms of high-temperature superconductivity and quantum spin liquids. His recent research interests are also in developing accurate quantum many-body solvers aided by human-interpretable artificial intelligence to reveal the structure of emergent quantum matter with quantum entanglement by mapping quantum many-body states onto artificial-intelligence architectures.