Zobrazeno 1 - 10
of 1 419
pro vyhledávání: '"Wall, Michael A"'
Autor:
Fattebert, Jean-Luc, Negre, Christian F. A., Finkelstein, Joshua, Mohd-Yusof, Jamaludin, Osei-Kuffuor, Daniel, Wall, Michael E., Zhang, Yu, Bock, Nicolas, Mniszewski, Susan M.
To address the challenge of performance portability, and facilitate the implementation of electronic structure solvers, we developed the Basic Matrix Library (BML) and Parallel, Rapid O(N) and Graph-based Recursive Electronic Structure Solver (PROGRE
Externí odkaz:
http://arxiv.org/abs/2401.13772
Publikováno v:
Phys. Rev. B 110, 214402 (2024)
We investigate quantum algorithms derived from tensor networks to simulate the static and dynamic properties of quantum many-body systems. Using a sequentially prepared quantum circuit representation of a matrix product state (MPS) that we call a qua
Externí odkaz:
http://arxiv.org/abs/2309.15165
Publikováno v:
Phys. Rev. A 109, 013318 (2024)
The experimental realization of Fermi-Hubbard tweezer arrays opens a new stage for engineering fermionic matter, where programmable lattice geometries and Hubbard model parameters are combined with single-site imaging. In order to use these versatile
Externí odkaz:
http://arxiv.org/abs/2306.03019
Autor:
Cavender, Chapin E., Case, David A., Chen, Julian C. -H., Chong, Lillian T., Keedy, Daniel A., Lindorff-Larsen, Kresten, Mobley, David L., Ollila, O. H. Samuli, Oostenbrink, Chris, Robustelli, Paul, Voelz, Vincent A., Wall, Michael E., Wych, David C., Gilson, Michael K.
This review article provides an overview of structurally oriented, experimental datasets that can be used to benchmark protein force fields, focusing on data generated by nuclear magnetic resonance (NMR) spectroscopy and room temperature (RT) protein
Externí odkaz:
http://arxiv.org/abs/2303.11056
Graph-based linear scaling electronic structure theory for quantum-mechanical molecular dynamics simulations is adapted to the most recent shadow potential formulations of extended Lagrangian Born-Oppenheimer molecular dynamics, including fractional
Externí odkaz:
http://arxiv.org/abs/2212.01997
Autor:
Wittwer, Felix, Sauter, Nicholas K., Mendez, Derek, Poon, Billy K., Brewster, Aaron S., Holton, James M., Wall, Michael E., Hart, William E., Bard, Deborah J., Blaschke, Johannes P.
The upcoming exascale computing systems Frontier and Aurora will draw much of their computing power from GPU accelerators. The hardware for these systems will be provided by AMD and Intel, respectively, each supporting their own GPU programming model
Externí odkaz:
http://arxiv.org/abs/2205.07976
Autor:
Bin Saif, Anas, Summerour, Virginia, Al-Saadi, Nina, Arif, Anum, Newman, Jeremy, Wall, Michael
Publikováno v:
In Annals of Vascular Surgery December 2024 109:522-530
We demonstrate the use of matrix product state (MPS) models for discriminating quantum data on quantum computers using holographic algorithms, focusing on classifying a translationally invariant quantum state based on $L$ qubits of quantum data extra
Externí odkaz:
http://arxiv.org/abs/2202.10911
Autor:
Mniszewski, Susan M, Belak, James, Fattebert, Jean-Luc, Negre, Christian FA, Slattery, Stuart R, Adedoyin, Adetokunbo A, Bird, Robert F, Chang, Choongseok, Chen, Guangye, Ethier, Stephane, Fogerty, Shane, Habib, Salman, Junghans, Christoph, Lebrun-Grandie, Damien, Mohd-Yusof, Jamaludin, Moore, Stan G, Osei-Kuffuor, Daniel, Plimpton, Steven J, Pope, Adrian, Reeve, Samuel Temple, Ricketson, Lee, Scheinberg, Aaron, Sharma, Amil Y, Wall, Michael E
The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applications (CoPA) is addressing challenges faced by particle-based applica
Externí odkaz:
http://arxiv.org/abs/2109.09056
Autor:
Wall, Michael L., D'Aguanno, Giuseppe
Publikováno v:
Phys. Rev. A 104, 042408 (2021)
We describe a quantum-assisted machine learning (QAML) method in which multivariate data is encoded into quantum states in a Hilbert space whose dimension is exponentially large in the length of the data vector. Learning in this space occurs through
Externí odkaz:
http://arxiv.org/abs/2104.02249