AI Assistant Scientist: Aiding scientific discovery with quantum-like evolutionary algorithm

Lizhi Xin; Kevin Xin

doi:10.51094/jxiv.1103

##article.authors##

Lizhi Xin XINVISIONQ, INC.
Kevin Xin XINVISIONQ, INC. https://orcid.org/0009-0007-5745-7115

DOI:

https://doi.org/10.51094/jxiv.1103

キーワード:

scientific discovery、 genetic programming、 machine learning、 AI assistant scientist、 quantum-like decision theory、 quantum-like evolutionary algorithm

抄録

The greatest challenge of scientific discovery is how unknown becomes known. While there have been ways set forth to go about scientific discovery, most require rigorous work done by humans, and involve human scientists trying their best to sift out the most important information from vast amounts of raw data. This paper presents an AI assistant scientist that utilizes quantum superposition principle to model many different theories and applies genetic programming to evolve the most satisfactory theory from many possible ones. Setting out from an information perspective, multiple AI agents cooperate to find the valuable information from raw data: (1) produce a function for natural phenomena with complete information obtained from experimental data, and (2) produce a matrix for natural phenomena where complete information cannot be obtained from experimental data. Using the freefall trajectory of a light sphere and Schrodinger's Cat simulated thought experiment as case studies, we show that the AI assistant scientist is able to reconstruct the past trajectory and predict the future trajectory of the light sphere with the function it produces, and to reconstruct the cats' past states and probabilistically predict the cat's future states with the matrix it produces. We believe that the key to scientific discovery is how to obtain as much valuable information from raw data as possible and this can be done by the AI Assistant scientist that’s powered with the quantum-like evolutionary algorithm that we’ve developed.

利益相反に関する開示

The authors are affiliated with XINVISIONQ, INC., the developer of a commercial forecasting tool, but for transparency of the experiments conducted in this study, all data generated and produced are included in the main text and supplementary materials.

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AI Assistant Scientist

Aiding scientific discovery with quantum-like evolutionary algorithm