Quantum matter, the research field studying phases of matter whose properties are
intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter …

Restricted Boltzmann machines in quantum physics | Nature Physics Skip to main content Thank
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Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum
physics. NetKet is built around neural-network quantum states and provides efficient …

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 …

Pursuing fractionalized particles that do not bear properties of conventional measurable
objects, exemplified by bare particles in the vacuum such as electrons and elementary …

We show that neural quantum states based on very deep (4–16-layered) neural networks
can outperform state-of-the-art variational approaches on highly frustrated quantum …

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 …

We develop a theoretical framework for S n-equivariant convolutional quantum circuits with
SU (d) symmetry, building on and significantly generalizing Jordan's permutational quantum …

Neural quantum states (NQS) are a promising approach to study many-body quantum
physics. However, they face a major challenge when applied to lattice models: convolutional …