Finite temperature properties of strongly correlated systems via variational Monte Carlo

Abstract

Variational methods are a common approach for computing properties of ground states but have not yet found analogous success in finite temperature calculations. In this work we develop a new variational finite temperature algorithm (VAFT) which combines ideas from minimally entangled typical thermal states (METTS), variational Monte Carlo (VMC) optimization and path integral Monte Carlo (PIMC). This allows us to define an implicit variational density matrix to estimate finite temperature properties in two and three dimensions. We benchmark the algorithm on the bipartite Heisenberg model and compare to exact results.

9 pages

References in corpus (4)

• Minimally Entangled Typical Thermal State Algorithms
• Accurate exchange-correlation energies for the warm dense electron gas
• Complete-Graph Tensor Network States: A New Fermionic Wave Function Ansatz for Molecules
• Finite-Temperature Auxiliary-Field Quantum Monte Carlo for Bose-Fermi Mixtures

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• Ab initio Path Integral Monte Carlo Simulations of Quantum Dipole Systems in Traps: Superfluidity, Quantum Statistics, and Structural Properties
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• Variational Neural and Tensor Network Approximations of Thermal States
• Thermal coupled cluster theory for SU(2) systems