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

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.

Effect of different substituents on the water-solubility and stability properties of 1 : 2 [60]fullerene derivative·gamma-cyclodextrin complexes.

We have demonstrated that C60 derivatives bearing a pyrrolidine moiety as well as a variety of other substituents can form 1 : 2 complexes with γ-cyclodextrin (γ-CDx) using a mechanochemical high-speed vibration milling apparatus. When the influence of the steric hindrance of the substituents on the formation of the complexes was negligible, the water-solubilities of the complexes were shown experimentally to be completely dependent on the hydrophobic properties of the substituent. Furthermore, the stabilities of the γ-CDx-complexes of several different C60 derivatives were found to be similar to or slightly higher than that of the C60·γ-CDx complex, with the solubilities of the complexes showing no correlation to the stabilities.