All Projects → wangleiphy → BetaVQE.jl

wangleiphy / BetaVQE.jl

Licence: MIT license
Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits

Programming Languages

julia
2034 projects

Projects that are alternatives of or similar to BetaVQE.jl

pulsemaker
A Python widget library for designing pulses and pulse schedules for quantum computing hardware. Can be used in Jupyter notebooks, JupyterLab, and the IPython kernel.
Stars: ✭ 19 (-29.63%)
Mutual labels:  quantum-computing
stat-mech-van
Code for 'Solving Statistical Mechanics using Variational Autoregressive Networks'.
Stars: ✭ 73 (+170.37%)
Mutual labels:  autoregressive-neural-networks
Quantum-Machine-Learning
This repository contains implementations of Quantum Machine Learning algorithms, feature maps, variational circuits and research papers.
Stars: ✭ 47 (+74.07%)
Mutual labels:  quantum-computing
cirq-on-iqm
Cirq adapter for IQM's quantum computers
Stars: ✭ 21 (-22.22%)
Mutual labels:  quantum-computing
quil
Specification of Quil: A Practical Quantum Instruction Set Architecture
Stars: ✭ 80 (+196.3%)
Mutual labels:  quantum-computing
qisjob
Qiskit Job Control
Stars: ✭ 24 (-11.11%)
Mutual labels:  quantum-computing
qram
Library for Q# implementing various qRAM proposals
Stars: ✭ 46 (+70.37%)
Mutual labels:  quantum-computing
unitaryhack
Rules and information for the 2021 unitaryHACK event hosted by @unitaryfund
Stars: ✭ 16 (-40.74%)
Mutual labels:  quantum-computing
QuantumComputing
Collection of Tutorials and other Quantum Computer programming related things.
Stars: ✭ 120 (+344.44%)
Mutual labels:  quantum-computing
forest-benchmarking
A library for quantum characterization, verification, validation (QCVV), and benchmarking using pyQuil.
Stars: ✭ 41 (+51.85%)
Mutual labels:  quantum-computing
pennylane-lightning
The PennyLane-Lightning plugin provides a fast state-vector simulator written in C++ for use with PennyLane
Stars: ✭ 28 (+3.7%)
Mutual labels:  quantum-computing
pyQuirk
A Python widget for Quirk to be used in Jupyter notebooks, JupyterLab, and the IPython kernel.
Stars: ✭ 18 (-33.33%)
Mutual labels:  quantum-computing
cusp cirq demo
Demonstration of CUSP algorithm using Cirq
Stars: ✭ 35 (+29.63%)
Mutual labels:  quantum-computing
quantumcat
quantumcat is a platform-independent, open-source, high-level quantum computing library, which allows the quantum community to focus on developing platform-independent quantum applications without much effort.
Stars: ✭ 18 (-33.33%)
Mutual labels:  quantum-computing
Quantum-Computing-Opportunities
Moved to Gitlab
Stars: ✭ 43 (+59.26%)
Mutual labels:  quantum-computing
qcl
Quantum Computation Language port from http://tph.tuwien.ac.at/~oemer/qcl.html
Stars: ✭ 29 (+7.41%)
Mutual labels:  quantum-computing
jet
Jet is a cross-platform library for simulating quantum circuits using tensor network contractions.
Stars: ✭ 34 (+25.93%)
Mutual labels:  quantum-computing
Quantum-Computing-Resources
This repository contains the best resources for learning practical quantum computing. This repository will be updated frequently.
Stars: ✭ 60 (+122.22%)
Mutual labels:  quantum-computing
quantumjava
Samples related to "Quantum Computing for Java Developers"
Stars: ✭ 86 (+218.52%)
Mutual labels:  quantum-computing
qubovert
The one-stop package for formulating, simulating, and solving problems in boolean and spin form
Stars: ✭ 19 (-29.63%)
Mutual labels:  quantum-computing
logo

Solving Quantum Statistical Mechanics with Variational Autoregressive Networks and Quantum Circuits

CI

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

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].