Configured quantum reservoir computing for multi-task machine learning

Xia, Wei; Zou, Jie; Qiu, Xingze; Chen, Feng; Zhu, Bing; Li, Chunhe; Deng, Dong-Ling; Li, Xiaopeng

doi:10.1016/j.scib.2023.08.040

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Configured quantum reservoir computing for multi-task machine learning

Cited by 4 publications

References 48 publications

Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications

Quantum machine learning on near-term quantum devices: Current state of supervised and unsupervised techniques for real-world applications

Estimating many properties of a quantum state via quantum reservoir processing

Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators’ Software in an Industrial Context

Contact Info

Product

Resources

About