Green function monte carlo with stochastic reconfiguration
TLDR
In this article, a new method for the stabilization of the sign problem in the Green Function Monte Carlo technique is devised for real lattice Hamiltonians and is based on an iterative ''stochastic reconfiguration'' scheme which introduces some bias but allows a stable simulation with constant sign.Abstract:
A new method for the stabilization of the sign problem in the Green Function Monte Carlo technique is proposed. The method is devised for real lattice Hamiltonians and is based on an iterative ''stochastic reconfiguration'' scheme which introduces some bias but allows a stable simulation with constant sign. The systematic reduction of this bias is in principle possible. The method is applied to the frustrated J1-J2 Heisenberg model, and tested against exact diagonalization data. Evidence of a finite spin gap for J2/J1 >~ 0.4 is found in the thermodynamic limit.read more
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Machine learning and the physical sciences
Giuseppe Carleo,J. Ignacio Cirac,Kyle Cranmer,Laurent Daudet,Maria Schuld,Naftali Tishby,Leslie Vogt-Maranto,Lenka Zdeborová +7 more
TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Journal ArticleDOI
Neural-network quantum state tomography
Giacomo Torlai,Giacomo Torlai,Guglielmo Mazzola,Juan Carrasquilla,Matthias Troyer,Matthias Troyer,Roger G. Melko,Roger G. Melko,Giuseppe Carleo +8 more
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Journal ArticleDOI
Many-body quantum state tomography with neural networks
Giacomo Torlai,Guglielmo Mazzola,Juan Carrasquilla,Matthias Troyer,Roger G. Melko,Giuseppe Carleo +5 more
TL;DR: In this paper, machine learning techniques are used for quantum state tomography (QST) of highly entangled states, in both one and two dimensions, and the resulting approach allows one to reconstruct traditionally challenging many-body quantities - such as the entanglement entropy - from simple, experimentally accessible measurements.
Book
Quantum Monte Carlo Approaches for Correlated Systems
TL;DR: In this paper, a comprehensive introduction to state-of-the-art quantum Monte Carlo techniques relevant for applications in correlated systems is provided, providing a clear overview of variational wave functions, and featuring a detailed presentation of stochastic samplings including Markov chains and Langevin dynamics.
Journal ArticleDOI
Restricted Boltzmann machines in quantum physics
TL;DR: A restricted Boltzmann machine (RBM) has been widely used in artificial intelligence applications for decades as mentioned in this paper and is now finding new life in the simulation of complex wavefunctions in quantum many-body physics.