Machine-learned correction to ensemble-averaged wave packet dynamics

• For a detailed understanding of many processes in nature involving, for example, energy or electron transfer, the theory of open quantumsystems is of key importance. For larger systems, an accurate description of the underlying quantum dynamics is still a formidable task, and,hence, approaches employing machine learning techniques have been developed to reduce the computational effort of accurate dissipativequantum dynamics. A downside of many previous machine learning methods is that they require expensive numerical training datasets forsystems of the same size as the ones they will be employed on, making them unfeasible to use for larger systems where those calculationsare still too expensive. In this work, we will introduce a new method that is implemented as a machine-learned correction term to the socalled Numerical Integration of Schrödinger Equation (NISE) approach. It is shown that this term can be trained on data from small systemswhere accurate quantum methods are still numerically feasible. Subsequently, the NISE scheme, together with the new machine-learnedcorrection, can be used to determine the dissipative quantum dynamics for larger systems. Furthermore, we show that the newly proposedmachine-learned correction outperforms a previously handcrafted one, which, however, improves the results already considerably. 

Ämnesord

NATURVETENSKAP  -- Fysik -- Atom- och molekylfysik och optik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Atom and Molecular Physics and Optics (hsv//eng)

NATURVETENSKAP  -- Kemi -- Teoretisk kemi (hsv//swe)

NATURAL SCIENCES  -- Chemical Sciences -- Theoretical Chemistry (hsv//eng)

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