Local Measure of Quantum Effects in Quantum Dynamics

Temporally unstructured quantum computation

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2008.0443 ◽

2009 ◽

Vol 465 (2105) ◽

pp. 1413-1439 ◽

Cited By ~ 52

Author(s):

Dan Shepherd ◽

Michael J. Bremner

Keyword(s):

Quantum Computation ◽

Quantum Dynamics ◽

Probability Distributions ◽

Temporal Structure ◽

Quantum Effects ◽

Abstract Model ◽

Restricted Class ◽

Interactive Proof ◽

Binary Matroids ◽

Shor's Algorithm

We examine theoretic architectures and an abstract model for a restricted class of quantum computation, called here temporally unstructured (‘ instantaneous ’) quantum computation because it allows for essentially no temporal structure within the quantum dynamics. Using the theory of binary matroids, we argue that the paradigm is rich enough to enable sampling from probability distributions that cannot, classically, be sampled efficiently and accurately. This paradigm also admits simple interactive proof games that may convince a sceptic of the existence of truly quantum effects. Furthermore, these effects can be created using significantly fewer qubits than are required for running Shor's algorithm.

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Quantum Effects in the Dissociative Chemisorption of N2 on Fe(111): Full-Dimensional Quantum Dynamics and Quasi-Classical Trajectory Study

The Journal of Physical Chemistry C ◽

10.1021/acs.jpcc.1c05334 ◽

2021 ◽

Author(s):

Huixia Shi ◽

Tianhui Liu ◽

Yanlin Fu ◽

Xiaoxiao Lu ◽

Bina Fu ◽

...

Keyword(s):

Quantum Dynamics ◽

Quantum Effects ◽

Classical Trajectory ◽

Dissociative Chemisorption

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Spiers Memorial Lecture : Introductory lecture: quantum dynamics of chemical reactions

Faraday Discussions ◽

10.1039/c8fd00131f ◽

2018 ◽

Vol 212 ◽

pp. 9-32 ◽

Cited By ~ 4

Author(s):

David C. Clary

Keyword(s):

Small Molecules ◽

Chemical Reactions ◽

Quantum Dynamics ◽

Quantum Effects ◽

Memorial Lecture ◽

Introductory Lecture

This Spiers Memorial Lecture discusses quantum effects that can be calculated and observed in the chemical reactions of small molecules.

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Predicting the future of excitation energy transfer in light-harvesting complex with artificial intelligence-based quantum dynamics

10.26434/chemrxiv-2021-d2ksx ◽

2021 ◽

Author(s):

Arif Ullah ◽

Pavlo O. Dral

Keyword(s):

Artificial Intelligence ◽

Energy Transfer ◽

Excitation Energy ◽

Quantum Dynamics ◽

Light Harvesting ◽

Computational Cost ◽

Excitation Energy Transfer ◽

Reorganization Energy ◽

Quantum Effects ◽

Light Harvesting Complexes

Exploring excitation energy transfer (EET) in light-harvesting complexes (LHCs) is essential for understanding the natural processes and design of highly-efficient photovoltaic devices. LHCs are open systems, where quantum effects may play a crucial role for almost perfect utilization of solar energy. Simulation of energy transfer with inclusion of quantum effects can be done within the framework of dissipative quantum dynamics (QD), which are computationally expensive. Thus, artificial intelligence (AI) offers itself as a tool for reducing the computational cost. We suggest AI-QD approach using AI to directly predict QD as a function of time and other parameters such as temperature, reorganization energy, etc., completely circumventing the need of recursive step-wise dynamics propagation in contrast to the traditional QD and alternative, recursive AI-based QD approaches. Our trajectory-learning AI-QD approach is able to predict the correct asymptotic behavior of QD at infinite time. We demonstrate AI-QD on seven-sites Fenna–Matthews–Olson (FMO) complex.

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