262 citations · 1.3k across the 31 of their papers we have counts for
31 papers
Retentive Neural Quantum States: Efficient Ansätze for Ab Initio Quantum Chemistry
Oliver Knitter, Dan Zhao, James Stokes +3
Neural-network quantum states (NQS) has emerged as a powerful application of quantum-inspired deep learning for variational Monte Carlo methods, offering a competitive alternative…
LLM-initialized Differentiable Causal Discovery
Shiv Kampani, David Hidary, Constantijn van der Poel +2
The discovery of causal relationships between random variables is an important yet challenging problem that has applications across many scientific domains. Differentiable causal d…
Density Matrix Renormalization Group with Tensor Processing Units
Martin Ganahl, Jackson Beall, Markus Hauru +4
Google's Tensor Processing Units (TPUs) are integrated circuits specifically built to accelerate and scale up machine learning workloads. They can perform fast distributed matrix m…
Dynamics of Transmon Ionization
Ross Shillito, Alexandru Petrescu, Joachim Cohen +6
Qubit measurement and control in circuit QED rely on microwave drives, with higher drive amplitudes ideally leading to faster processes. However, degradation in qubit coherence tim…
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'…