Antn: Bridging autoregressive neural networks and tensor networks for quantum many-body simulation
Quantum many-body physics simulation has important impacts on understanding
fundamental science and has applications to quantum materials design and quantum …
fundamental science and has applications to quantum materials design and quantum …
Variational neural-network ansatz for continuum quantum field theory
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 …
principle to quantum field theories. In nonrelativistic quantum field theories, the challenge is …
Symmetric tensor networks for generative modeling and constrained combinatorial optimization
Constrained combinatorial optimization problems abound in industry, from portfolio
optimization to logistics. One of the major roadblocks in solving these problems is the …
optimization to logistics. One of the major roadblocks in solving these problems is the …
Q-flow: generative modeling for differential equations of open quantum dynamics with normalizing flows
Studying the dynamics of open quantum systems can enable breakthroughs both in
fundamental physics and applications to quantum engineering and quantum computation …
fundamental physics and applications to quantum engineering and quantum computation …
Deep learning lattice gauge theories
Monte Carlo methods have led to profound insights into the strong-coupling behavior of
lattice gauge theories and produced remarkable results such as first-principles computations …
lattice gauge theories and produced remarkable results such as first-principles computations …
Gauged Gaussian projected entangled pair states: A high dimensional tensor network formulation for lattice gauge theories
Gauge theories form the basis of our understanding of modern physics—ranging from the
description of quarks and gluons to effective models in condensed matter physics. In the …
description of quarks and gluons to effective models in condensed matter physics. In the …
Simulating 2+ 1d lattice quantum electrodynamics at finite density with neural flow wavefunctions
We present a neural flow wavefunction, Gauge-Fermion FlowNet, and use it to simulate 2+
1D lattice compact quantum electrodynamics with finite density dynamical fermions. The …
1D lattice compact quantum electrodynamics with finite density dynamical fermions. The …
Approximately-symmetric neural networks for quantum spin liquids
We propose and analyze a family of approximately-symmetric neural networks for quantum
spin liquid problems. These tailored architectures are parameter-efficient, scalable, and …
spin liquid problems. These tailored architectures are parameter-efficient, scalable, and …
Pairing-based graph neural network for simulating quantum materials
We introduce a pairing-based graph neural network, $\textit {GemiNet} $, for simulating
quantum many-body systems. Our architecture augments a BCS mean-field wavefunction …
quantum many-body systems. Our architecture augments a BCS mean-field wavefunction …
Variational Monte Carlo algorithm for lattice gauge theories with continuous gauge groups: A study of -dimensional compact QED with dynamical fermions at …
Lattice gauge theories coupled to fermionic matter account for many interesting phenomena
in both high-energy physics and condensed-matter physics. Certain regimes, eg, at finite …
in both high-energy physics and condensed-matter physics. Certain regimes, eg, at finite …