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Title: Resource Optimization for Quantum Dynamics with Tensor Networks: Quantum and Classical Algorithms
The exponential scaling of the quantum degrees of freedom with the size of the system is one of the biggest challenges in computational chemistry and particularly in quantum dynamics. We present a tensor network approach for the time-evolution of the nuclear degrees of freedom of multiconfigurational chemical systems at a reduced storage and computational complexity. We also present quantum algorithms for the resultant dynamics. To preserve the compression advantage achieved via tensor network decompositions, we present an adaptive algorithm for the regularization of nonphysical bond dimensions, preventing the potentially exponential growth of these with time. While applicable to any quantum dynamical problem, our method is particularly valuable for dynamical simulations of nuclear chemical systems. Our algorithm is demonstrated using ab initio potentials obtained for a symmetric hydrogen-bonded system, namely, the protonated 2,2′-bipyridine, and compared to exact diagonalization numerical results.  more » « less
Award ID(s):
1936353 2102610
PAR ID:
10550317
Author(s) / Creator(s):
; ;
Publisher / Repository:
ACS Publications
Date Published:
Journal Name:
The Journal of Physical Chemistry A
Volume:
128
Issue:
32
ISSN:
1089-5639
Page Range / eLocation ID:
6774 to 6797
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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