LeapfrogLayers: A Trainable Framework for Effective Topological Sampling

1 code implementation • 2 Dec 2021 • Sam Foreman, Xiao-Yong Jin, James C. Osborn

We introduce LeapfrogLayers, an invertible neural network architecture that can be trained to efficiently sample the topology of a 2D $U(1)$ lattice gauge theory.

HMC with Normalizing Flows

1 code implementation • 2 Dec 2021 • Sam Foreman, Taku Izubuchi, Luchang Jin, Xiao-Yong Jin, James C. Osborn, Akio Tomiya

We propose using Normalizing Flows as a trainable kernel within the molecular dynamics update of Hamiltonian Monte Carlo (HMC).

Deep Learning Hamiltonian Monte Carlo

1 code implementation • 7 May 2021 • Sam Foreman, Xiao-Yong Jin, James C. Osborn

We generalize the Hamiltonian Monte Carlo algorithm with a stack of neural network layers and evaluate its ability to sample from different topologies in a two dimensional lattice gauge theory.

Quantum Circuits: Divide and Compute with Maximum Likelihood Tomography

1 code implementation • 22 May 2020 • Michael A. Perlin, Zain H. Saleem, Martin Suchara, James C. Osborn

We introduce maximum likelihood fragment tomography (MLFT) as an improved circuit cutting technique for running "clustered" quantum circuits on quantum devices with a limited number of qubits.

Quantum Physics