Noise-robust optimization of quantum machine learning models for polymer properties using a simulator and validated on the IonQ quantum computer.

ABSTRACT: Quantum machine learning for predicting the physical properties of polymer materials based on the molecular descriptors of monomers was investigated. Under the stochastic variation of the expected predicted values obtained from quantum circuits due to finite sampling, the methods proposed in previous works did not make sufficient progress in optimizing the parameters. To enable parameter optimization despite the presence of stochastic variations in the expected values, quantum circuits that improve prediction accuracy without increasing the number of parameters and parameter optimization methods that are robust to stochastic variations in the expected predicted values, were investigated. The multi-scale entanglement renormalization ansatz circuit improved the prediction accuracy without in

SUBMITTER: Ishiyama Y 

PROVIDER: S-EPMC9643424 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature