Quantum circuit learning as a potential algorithm to predict experimental chemical properties

24 August 2022, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


We introduce quantum circuit learning (QCL) as an emerging regression algorithm for chemo- and materials-informatics. The supervised model, functioning on the rule of quantum mechanics, can process linear and smooth non-linear functions from small datasets (< 100 records). Compared with conventional algorithms, such as random forest, support vector machine, and linear regressions, the QCL can offer better predictions with some one-dimensional functions and experimental chemical databases. QCL will potentially help the virtual exploration of new molecules and materials more efficiently through its superior prediction performances.


Quantum computing
machine learning
materials informatics

Supplementary weblinks


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