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Progress of Theoretical Physics

Stochastic Processes in Microphysics in Connection with Relativity

J. C. Aron

Published Apr 1, 1966• 0 citations
Publisher
DOI
10.1143/ptp.35.757a

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Stochastic Processes in Microphysics in Connection with Relativity

J. C. Aron

The difficulties of extending to relativity the results obtained in a preceding paper are set forth; in particular the relativistic behaviour of the time and length minimal constants brings us to a well-known problem. The proposed views attempt to deal with these equations. They give a relativistic generalization of the classical diffusion theory, from which the Klein-Gordon equation is deduced. Consistent results are obtained by a relativistic survey of Dirac's fluid model. Subsequent investigations have been carried out concerning the character of relativity and the limitation of its validity in microphysics.

Progress of Theoretical Physics•Jan 1, 1966•5 citations
Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of actual improvements and scalability, partly due to phenomena such as barren plateaus. In this paper, we employ a hybrid quantum neural network (QNN) on a dataset on cloud microphysics, containing processes for phase transitions of water in the atmosphere and its related temperature changes, which are highly relevant for accurate climate predictions and projections. To reach optimal performance of our QNNs, we employ a rich and trainable frequency spectrum together with expressivity enhancing classical postprocessing. We find that our QNNs strongly benefit from extensive hyperparameter optimization and thereby demonstrate the feasibility of applying QNNs to complex physical systems. At the same time, the QNNs are outperformed by classical baselines in the form of simple fully-connected neural networks. We discuss identified bottlenecks of this class of quantum models to learn the full complexity of the cloud microphysics dataset to show that there is a need to further understand and improve variational quantum models for machine learning such that they might fill the gap where classical models fail or are inefficient.
The difficulties of extending to relativity the results obtained in a preceding paper are set forth; in particular the relativistic behaviour of the time and length minimal constants brings us to a well-known problem. The proposed views attempt to deal with these equations. They give a relativistic generalization of the classical diffusion theory, from which the Klein-Gordon equation is deduced. Consistent results are obtained by a relativistic survey of Dirac's fluid model. Subsequent investigations have been carried out concerning the character of relativity and the limitation of its validity in microphysics.