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Paper 1

Addendum to "Single photon logic gates using minimum resources"

Qing Lin, Bing He

Year

2010

Journal

arXiv preprint

DOI

arXiv:1011.4814

arXiv

1011.4814

The authors call attention to a previous work [Qing Lin and Bing He, Phys. Rev. A 80, 042310 (2009)] on the realization of multi-qubit logic gates with controlled-path and merging gate. We supplement the work by showing how to efficiently build realistic quantum circuits in this approach and giving the guiding rules for the task.

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Paper 2

Rapid Prediction of Hot-Carrier Relaxation by Learning of Nonadiabatic Hamiltonians with Graph Neural Networks.

Meng K, Lu H, Xu X, Prezhdo OV, Long R

Year

2026

Journal

Journal of chemical theory and computation

DOI

10.1021/acs.jctc.5c02178

arXiv

-

An electron-vibrational Hamiltonian fully encodes corresponding quantum dynamics; however, extracting the dynamics still relies on time and memory-consuming trajectory-based nonadiabatic molecular dynamics (NAMD) simulations, typically stochastic surface hopping. Here, we develop a general graph neural network, artificial intelligence ab initio NAMD (AINAMD) that establishes an end-to-end mapping from Hamiltonian to hot carrier relaxation dynamics. We validated the generality of AINAMD across multiple materials, including a zero-dimensional Si quantum dot (QD), a one-dimensional carbon nanotube (CNT), a two-dimensional twisted MoS/WS bilayer, and a three-dimensional soft-lattice MAPbI perovskite. With only 10% training data, AINAMD can rapidly and accurately generate picosecond energy decay curves for hot electron and hot hole relaxation for the remaining 90% Hamiltonians, while delivering a computational speed-up of more than 6 orders of magnitude compared to standard CPU-based NAMD simulations. Moreover, AINAMD can also map directly the Hamiltonian to the carrier relaxation time, bypassing generation of the energy decay curves and demonstrating the ability to handle complex NAMD tasks. Further, by projecting high-dimensional Hamiltonian encoding features into a two-dimensional space with unsupervised learning, we demonstrate that AINAMD can effectively distinguish Hamiltonian types, verifying its ability to identify a particular system (QD, CNT, MoS/WS and MAPbI) and a charge carrier (electron or hole). Overall, the developed AINAMD approach provides a novel computational methodology and a conceptual framework for accelerating NAMD simulations with machine learning by many orders of magnitude.

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