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Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits

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Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits

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Setup

Typing ] in a Julia REPL, and then

pkg> dev https://github.com/wangleiphy/BetaVQE.jl.git

To make sure BetaVQE is installed properly, type

pkg> test BetaVQE

Run

Run this to train the transverse field Ising model, open a terminal and type

$ cd ~/.julia/dev/BetaVQE

$ julia --project runner.jl learn 2 2 2.0 2.0

For Windows operation system, the Julia develop folder might be different.

This utility accepts the following arguments

  • nx::Int=2, lattice size in x direction,
  • ny::Int=2, lattice size in y direction,
  • Γ::Real=1.0, the strength of transverse field,
  • β::Real=1.0, inverse temperature,

and keyword arguments

  • depth::Int=5, circuit depth,
  • nsamples::Int=1000, the batch size used in training,
  • nhiddens::Vector{Int}=[500], dimension of the VAN's hidden layer,
  • lr::Real=0.01, the learning rate of the ADAM optimizer,
  • niter::Int=500, number of iteration,
  • cont::Bool=false, continue from checkpoint if true.

Paper

arXiv:1912.11381

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Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits

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