Zobrazeno 1 - 10
of 74
pro vyhledávání: '"Susan M Mniszewski"'
Publikováno v:
PLoS ONE, Vol 17, Iss 7, p e0271292 (2022)
The efficient calculation of the centrality or "hierarchy" of nodes in a network has gained great relevance in recent years due to the generation of large amounts of data. The eigenvector centrality (aka eigencentrality) is quickly becoming a good me
Externí odkaz:
https://doaj.org/article/f590d28cb02e41cd973d617a4f2c8739
Publikováno v:
PLoS ONE, Vol 17, Iss 2, p e0263849 (2022)
The most advanced D-Wave Advantage quantum annealer has 5000+ qubits, however, every qubit is connected to a small number of neighbors. As such, implementation of a fully-connected graph results in an order of magnitude reduction in qubit count. To c
Externí odkaz:
https://doaj.org/article/d6f32a3892b54fc48e2a6d57f15e83fc
Publikováno v:
PLoS ONE, Vol 17, Iss 5, p e0267954 (2022)
We describe an algorithm to compute the extremal eigenvalues and corresponding eigenvectors of a symmetric matrix which is based on solving a sequence of Quadratic Binary Optimization problems. This algorithm is robust across many different classes o
Externí odkaz:
https://doaj.org/article/ee95e45b4a3b45bf9e471fdbea267176
Publikováno v:
PLoS ONE, Vol 15, Iss 1, p e0226787 (2020)
Isomer search or molecule enumeration refers to the problem of finding all the isomers for a given molecule. Many classical search methods have been developed in order to tackle this problem. However, the availability of quantum computing architectur
Externí odkaz:
https://doaj.org/article/ff64751908f645c99a0d97d09ffe9555
Publikováno v:
PLoS ONE, Vol 15, Iss 2, p e0227538 (2020)
A very important problem in combinatorial optimization is the partitioning of a network into communities of densely connected nodes; where the connectivity between nodes inside a particular community is large compared to the connectivity between node
Externí odkaz:
https://doaj.org/article/64b3c49594484086be77cd540ac75a98
Autor:
Justin S. Smith, Susan M. Mniszewski, Kipton Barros, Joshua Finkelstein, Anders M. N. Niklasson, Christian F. A. Negre, Emanuel H. Rubensson
Publikováno v:
Journal of Chemical Theory and Computation. 17:6180-6192
Tensor cores, along with tensor processing units, represent a new form of hardware acceleration specifically designed for deep neural network calculations in artificial intelligence applications. Tensor cores provide extraordinary computational speed
Autor:
Justin S. Smith, Emanuel H. Rubensson, Christian F. A. Negre, Kipton Barros, Susan M. Mniszewski, Joshua Finkelstein, Anders M. N. Niklasson
Publikováno v:
Journal of Chemical Theory and Computation. 17:2256-2265
We present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core units. A performance of over 100 teraFLOPs is achieved for half-precisi
Autor:
Ilya Safro, Hayato Ushijima-Mwesigwa, Ruslan Shaydulin, Susan M. Mniszewski, Yuri Alexeev, Christian F. A. Negre
Publikováno v:
ACM Transactions on Quantum Computing. 2:1-29
Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the post Moore’s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world dataset
Autor:
Sergei Tretiak, Yu Zhang, Christian F. A. Negre, Susan M. Mniszewski, Petr M. Anisimov, Pavel A. Dub
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Scientific Reports
Scientific Reports
Quantum chemistry is interested in calculating ground and excited states of molecular systems by solving the electronic Schrödinger equation. The exact numerical solution of this equation, frequently represented as an eigenvalue problem, remains unf
Autor:
Joshua Finkelstein, Emanuel H. Rubensson, Susan M. Mniszewski, Christian F. A. Negre, Anders M. N. Niklasson
Publikováno v:
Journal of chemical theory and computation. 18(7)
Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer