Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in computer science

Time Propagation of Many-Body Quantum States on Graphics Processing Units

Topi Siro, Ari Harju

Published Jan 1, 2013• 1 citation
Publisher
DOI
10.1007/978-3-642-36803-5_10

Related papers

Quantum Chemistry on Graphics Processing Units

Andreas W. Götz, Thorsten Wölfle, Ross C. Walker

Annual reports in computational chemistry•Jan 1, 2010•38 citations

Counting defects in quantum computers with Graphics Processing Units

Bartłomiej Gardas, Andrzej Ptok

Journal of Computational Physics•Apr 9, 2018•2 citations

Massively parallel split-step Fourier techniques for simulating quantum systems on graphics processing units

James Schloss

•Dec 27, 2019•2 citations

Exploring complex quantum systems with a hybrid CPU-GPU computing platform

A. Yu. Polyakov, S. Denisov, Peter Hänggi A

One of the most striking features of quantum mechanics is the exponential growth of resources, required to find the states of a composite system, with the size of the system. This also is the origin of the two main bottlenecks in numerical studies of complex quantum systems, that are (i) diagonalizations of big matrices and (ii) propagations of large systems of linear differential equations with global symplectic structure. Operations of the first type are purely scalable, while most of the propagation algorithms allow for the high degree of parallelism. Here we show how the workload of finding Floquet eigenstates of an ac-driven non- integrable quantum system can be shared between a general-purpose central processing unit (CPU) and a graphic processing unit (GPU), when both are working within one computing platform. Namely, diagonalization steps are delegated to the CPU, while the time propagation is performed on the GPU. This strategy led to a computational time speed-up of several order of magnitude as compared to the performance of the CPU alone.

•Jan 1, 2013•0 citations

Many-body quantum chemistry on graphics processing units

A. Eugene DePrince, Jeff R. Hammond, Stephen K. Gray

Heterogeneous nodes composed of a multicore CPU and at least one graphics processing unit (GPU) are increasingly common in high-performance scientific computing, and significant programming effort is currently being undertaken to port existing scientific algorithms to these unique architectures. We present implementations for two many-body quantum chemistry methods on heterogeneous nodes: the coupled-cluster with single and double excitations (CCSD) and time-dependent configuration interaction with single and double excitations (TD-CISD) methods. Both methods can be implemented on a computer as a series of dense matrix-matrix multiplications, operations that GPUs are particularly adept at performing. The GPU-accelerated CCSD algorithm is as much as 4.3 times faster than the corresponding CPU algorithm and 9.7 times faster than the algorithm in the Molpro package. The TD-CISD algorithm is accelerated by as much as a factor of 3.9 by GPUs. Enhanced performance is achieved by overlapping CPU and GPU computations.

•Jan 1, 2011•1 citation

Computer graphics for quantum computation

Solvita Zariņa, Rūsiņš Freivalds

ACM SIGGRAPH Posters•Aug 11, 2008•0 citations
One of the most striking features of quantum mechanics is the exponential growth of resources, required to find the states of a composite system, with the size of the system. This also is the origin of the two main bottlenecks in numerical studies of complex quantum systems, that are (i) diagonalizations of big matrices and (ii) propagations of large systems of linear differential equations with global symplectic structure. Operations of the first type are purely scalable, while most of the propagation algorithms allow for the high degree of parallelism. Here we show how the workload of finding Floquet eigenstates of an ac-driven non- integrable quantum system can be shared between a general-purpose central processing unit (CPU) and a graphic processing unit (GPU), when both are working within one computing platform. Namely, diagonalization steps are delegated to the CPU, while the time propagation is performed on the GPU. This strategy led to a computational time speed-up of several order of magnitude as compared to the performance of the CPU alone.
Heterogeneous nodes composed of a multicore CPU and at least one graphics processing unit (GPU) are increasingly common in high-performance scientific computing, and significant programming effort is currently being undertaken to port existing scientific algorithms to these unique architectures. We present implementations for two many-body quantum chemistry methods on heterogeneous nodes: the coupled-cluster with single and double excitations (CCSD) and time-dependent configuration interaction with single and double excitations (TD-CISD) methods. Both methods can be implemented on a computer as a series of dense matrix-matrix multiplications, operations that GPUs are particularly adept at performing. The GPU-accelerated CCSD algorithm is as much as 4.3 times faster than the corresponding CPU algorithm and 9.7 times faster than the algorithm in the Molpro package. The TD-CISD algorithm is accelerated by as much as a factor of 3.9 by GPUs. Enhanced performance is achieved by overlapping CPU and GPU computations.