Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

There has been enormous progress in the last few years in designing neural networks that
respect the fundamental symmetries and coordinate freedoms of physical law. Some of …

Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

Recurrent neural networks (RNNs), originally developed for natural language processing,
hold great promise for accurately describing strongly correlated quantum many-body …

Finding ground states of quantum many-body systems is known to be hard for both classical
and quantum computers. As a result, when Nature cools a quantum system in a low …

Neural networks are a promising tool for simulating quantum many body systems. Recently,
it has been shown that neural network-based models describe quantum many body systems …

Physicists dating back to Feynman have lamented the difficulties of applying the variational
principle to quantum field theories. In nonrelativistic quantum field theories, the challenge is …

The quantum many-body problem lies at the center of the most important open challenges in
condensed matter, quantum chemistry, atomic, nuclear, and high-energy physics. While …

Constrained combinatorial optimization problems abound in industry, from portfolio
optimization to logistics. One of the major roadblocks in solving these problems is the …