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

Kan Hatakeyama‐Sato

doi:10.1039/d2dd00090c

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

2023-01-01

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DOI：10.1039/d2dd00090c

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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 circuit learning as a potential algorithm to predict experimental chemical properties

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