96 citations · 299 across the 12 of their papers we have counts for
13 papers
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'…
Simulation of quantum physics with Tensor Processing Units: brute-force computation of ground states and time evolution
Markus Hauru, Alan Morningstar, Jackson Beall +3
Tensor Processing Units (TPUs) were developed by Google exclusively to support large-scale machine learning tasks. TPUs can, however, also be used to accelerate and scale up other…
Simulation of quantum many-body dynamics with Tensor Processing Units: Floquet prethermalization
Alan Morningstar, Markus Hauru, Jackson Beall +4
Tensor Processing Units (TPUs) are specialized hardware accelerators developed by Google to support large-scale machine-learning tasks, but they can also be leveraged to accelerate…
Quantum Computation with Machine-Learning-Controlled Quantum Stuff
Lucien Hardy, Adam G. M. Lewis
We describe how one may go about performing quantum computation with arbitrary "quantum stuff", as long as it has some basic physical properties. Imagine a long strip of stuff, equ…
Classical Simulations of Quantum Field Theory in Curved Spacetime I: Fermionic Hawking-Hartle Vacua from a Staggered Lattice Scheme
Adam G. M. Lewis, Guifré Vidal
We numerically compute renormalized expectation values of quadratic operators in a quantum field theory (QFT) of free Dirac fermions in curved two-dimensional (Lorentzian) spacetim…
Hadamard Renormalization of a 2-Dimensional Dirac Field
Adam G. M. Lewis
The Hadamard renormalization procedure is applied to a free, massive Dirac field on a 2 dimensional Lorentzian spacetime. This yields the state-independent divergent terms in t…
Automatic generation of CUDA code performing tensor manipulations using C++ expression templates
Adam G. M. Lewis, Harald P. Pfeiffer
We present a C++ library, TLoops, which uses a hierarchy of expression templates to represent operations upon tensorial quantities in single lines of C++ code that resemble analyti…
GPU-Accelerated Simulations of Isolated Black Holes
Adam G. M. Lewis, Harald P. Pfeiffer
We present a port of the numerical relativity code SpEC which is capable of running on NVIDIA GPUs. Since this code must be maintained in parallel with SpEC itself, a primary desig…
Fundamental frequencies and resonances from eccentric and precessing binary black hole inspirals
Adam G. M. Lewis, Aaron Zimmerman, Harald P. Pfeiffer
Binary black holes which are both eccentric and undergo precession remain unexplored in numerical simulations. We present simulations of such systems which cover about 50 orbits at…
Redshift factor and the first law of binary black hole mechanics in numerical simulations
Aaron Zimmerman, Adam G. M. Lewis, Harald P. Pfeiffer
The redshift factor is an invariant quantity of fundamental interest in post-Newtonian and self-force descriptions of compact binaries. It connects different approximation sche…