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no code implementations • 11 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.

no code implementations • 28 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.

1 code implementation • 15 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.

no code implementations • 9 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.

no code implementations • 20 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.

no code implementations • 31 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.

1 code implementation • 9 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.

1 code implementation • 4 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.

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.

no code implementations • 19 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.

no code implementations • 16 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.

1 code implementation • 5 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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