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.

Chemical-state analysis of organic semiconductors using soft X-ray absorption spectroscopy combined with first-principles calculation.

The chemical states of organic semiconductors were investigated by total-electron-yield soft X-ray absorption spectroscopy (TEY-XAS) and first-principles calculations. The organic semiconductors, pentacene (C(22)H(14)) and pentacenequinone (C(22)H(12)O(2)), were subjected to TEY-XAS and the experimental spectra obtained were compared with the 1s core-level excited spectra of C and O atoms, calculated by a first-principles planewave pseudopotential method. Excellent agreement between the measured and the calculated spectra were obtained for both materials.