Numerical and geometrical aspects of flow-based variational quantum Monte Carlo
Not on the map. Why?
Abstract (arXiv)
This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for pedagogical purposes on the example of bosons in the field amplitude (quadrature) basis. Particular emphasis is placed on the variational real- and imaginary-time evolution problems, carefully reviewing the stochastic estimation of the time-dependent variational principles and their relationship with information geometry. Some practical instructions are provided to guide the implementation of a PyTorch code. The review is intended to be accessible to researchers interested in machine learning and quantum information science.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 49 references that are in the map. First posted on arXiv 28 Mar 2022.
Builds on
21 of 49 references.
References · 21
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
- 2015Variational Inference with Normalizing Flowscited by 2,161
- 2016Density estimation using Real NVPcited by 2,029
- 2019Normalizing Flows for Probabilistic Modeling and Inferencecited by 1,315
- 2019Flow-based generative models for Markov chain Monte Carlo in lattice field theorycited by 147
- 2020Normalizing Flows on Tori and Spherescited by 117
- 2019Quantum Natural Gradientcited by 104
- 2020Equivariant flow-based sampling for lattice gauge theorycited by 102
- 2016Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiationcited by 93
- 2019BackPACK: Packing more into backpropcited by 77
9 more
- 2019Deep autoregressive models for the efficient variational simulation of many-body quantum systemscited by 64
- 2021Flow-based sampling for multimodal and extended-mode distributions in lattice field theorycited by 25
- 2021Ab-initio study of interacting fermions at finite temperature with neural canonical transformationcited by 16
- 2021Overcoming barriers to scalability in variational quantum Monte Carlocited by 10
- 2019Inferring the quantum density matrix with machine learningcited by 8
- 2021Rayleigh-Gauss-Newton optimization with enhanced sampling for variational Monte Carlocited by 6
- 2020Integrable Nonparametric Flowscited by 3
- 2021Continuous-variable neural-network quantum states and the quantum rotor modelcited by 2
- 2021Towards a variational Jordan-Lee-Preskill quantum algorithmcited by 1
Built on by
Citing papers per year: 2024: 1.
1 paper.