262 citations · 1.3k across the 31 of their papers we have counts for
5 papers · 1 filter
Large scale quantum chemistry with Tensor Processing Units
Ryan Pederson, John Kozlowski, Ruyi Song +8
We demonstrate the use of Google's cloud-based Tensor Processing Units (TPUs) to accelerate and scale up conventional (cubic-scaling) density functional theory (DFT) calculations.…
Large Scale Distributed Linear Algebra With Tensor Processing Units
Adam G. M. Lewis, Jackson Beall, Martin Ganahl +3
We have repurposed Google Tensor Processing Units (TPUs), application-specific chips developed for machine learning, into large-scale dense linear algebra supercomputers. The TPUs'…
TensorNetwork on TensorFlow: Entanglement Renormalization for quantum critical lattice models
Martin Ganahl, Ashley Milsted, Stefan Leichenauer +2
We use TensorNetwork [C. Roberts et al., arXiv: 1905.01330], a recently developed API for performing tensor network contractions using accelerated backends such as TensorFlow, to i…
TensorNetwork: A Library for Physics and Machine Learning
Chase Roberts, Ashley Milsted, Martin Ganahl +6
TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body…
Continuous Matrix Product States for Inhomogeneous Quantum Field Theories: a Basis-Spline Approach
Martin Ganahl
Continuous Matrix Product States (cMPS) are powerful variational ansatz states for ground states of continuous quantum field theories in (1+1) dimension. In this paper we introduce…