common.title

Docs
Quantum Circuit
TYTAN CLOUD

QUANTUM GAMING


Desktop RAG

Overview
Terms of service

Privacy policy

Contact
Research

Sign in
Sign up
common.title

Noise-robust optimization of quantum machine learning models for polymer properties using a simulator and validated on the IonQ quantum computer, November 2022

blueqat research

2022/11/09 02:48

Yuki Ishiyama, Ryutaro Nagai, Shunsuke Mieda, Yuki Takei, Yuichiro Minato & Yutaka Natsume , November 2022

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 increasing the number of parameters. The stochastic gradient descent method using the parameter-shift rule for gradient calculation was shown to be robust to sampling variability in the expected value. Finally, the quantum machine learning model was trained on an actual ion-trap quantum computer. At each optimization step, the coefficient of determination R2 improved equally on the actual machine and simulator, indicating that our findings enable the training of quantum circuits on the actual quantum computer to the same extent as on the simulator.

Scientific Reports

© 2025, blueqat Inc. All rights reserved