Search Results for author: James Stokes

Found 12 papers, 4 papers with code

Scalable neural quantum states architecture for quantum chemistry

no code implementations11 Aug 2022 Tianchen Zhao, James Stokes, Shravan Veerapaneni

Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems.

Numerical and geometrical aspects of flow-based variational quantum Monte Carlo

no code implementations28 Mar 2022 James Stokes, Brian Chen, Shravan Veerapaneni

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.

Continuous-variable neural-network quantum states and the quantum rotor model

1 code implementation15 Jul 2021 James Stokes, Saibal De, Shravan Veerapaneni, Giuseppe Carleo

We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization.

Quantization Variational Monte Carlo

Gauge equivariant neural networks for quantum lattice gauge theories

no code implementations9 Dec 2020 Di Luo, Giuseppe Carleo, Bryan K. Clark, James Stokes

Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials.

Meta Variational Monte Carlo

no code implementations20 Nov 2020 Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen, Shravan Veerapaneni

An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble.

Meta-Learning Variational Monte Carlo

Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states

no code implementations31 Jul 2020 James Stokes, Javier Robledo Moreno, Eftychios A. Pnevmatikakis, Giuseppe Carleo

First-quantized deep neural network techniques are developed for analyzing strongly coupled fermionic systems on the lattice.

Natural evolution strategies and variational Monte Carlo

1 code implementation9 May 2020 Tianchen Zhao, Giuseppe Carleo, James Stokes, Shravan Veerapaneni

A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization.

Combinatorial Optimization Variational Monte Carlo

Quantum Natural Gradient

1 code implementation4 Sep 2019 James Stokes, Josh Izaac, Nathan Killoran, Giuseppe Carleo

A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.

Mean-field Analysis of Batch Normalization

no code implementations ICLR 2019 Mingwei Wei, James Stokes, David J. Schwab

Batch Normalization (BatchNorm) is an extremely useful component of modern neural network architectures, enabling optimization using higher learning rates and achieving faster convergence.

Probabilistic Modeling with Matrix Product States

no code implementations19 Feb 2019 James Stokes, John Terilla

Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models.

Inductive Bias

Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks

no code implementations16 Feb 2018 Tengyuan Liang, James Stokes

Motivated by the pursuit of a systematic computational and algorithmic understanding of Generative Adversarial Networks (GANs), we present a simple yet unified non-asymptotic local convergence theory for smooth two-player games, which subsumes several discrete-time gradient-based saddle point dynamics.

Fisher-Rao Metric, Geometry, and Complexity of Neural Networks

1 code implementation5 Nov 2017 Tengyuan Liang, Tomaso Poggio, Alexander Rakhlin, James Stokes

We study the relationship between geometry and capacity measures for deep neural networks from an invariance viewpoint.

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