The Spin-MInt Algorithm: an Accurate and Symplectic Propagator for the Spin-Mapping Representation of Nonadiabatic Dynamics
Lauren E. Cook, James R. Rampton, Timothy J. H. Hele

TL;DR
The paper introduces the Spin-MInt algorithm, a novel symplectic propagator for spin-mapping nonadiabatic dynamics, which is more accurate and faster than previous methods, and rigorously preserves geometric structures.
Contribution
It presents the first symplectic algorithm for spin-mapping Hamiltonians that directly propagates spin variables without Cartesian transformation, improving accuracy and efficiency.
Findings
Spin-MInt is symplectic and preserves geometric structures.
It outperforms previous angle-based algorithms in accuracy.
It is faster than the MInt algorithm, especially for large systems.
Abstract
Mapping methods, including the Meyer-Miller-Stock-Thoss (MMST) mapping and spin-mapping, are commonly utilised to simulate nonadiabatic dynamics by propagating classical mapping variable trajectories. Recent work confirmed the Momentum Integral (MInt) algorithm is the only known symplectic algorithm for the MMST Hamiltonian. To our knowledge, no symplectic algorithm has been published for the spin-mapping representation without obtaining Cartesian variables and utilising the MInt algorithm. Here, we present the Spin-MInt algorithm which directly propagates the spin-mapping variables. First, we consider a two-level system which maps onto a spin-vector on a Bloch sphere. Despite the spin-variables being non-canonical, we rigorously prove the Spin-MInt is symplectic using a canonical variable transformation. We determine that the Spin-MInt is a symmetrical, second-order, time-reversible,…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Spectroscopy and Quantum Chemical Studies · Advanced NMR Techniques and Applications
