Hong-Ming Wang, Huan-Yu Ku, Jie-Yien Lin, Hong-Bin Chen, “Deep learning the hierarchy of steering measurement settings of qubit-pair states”, Commun Phys, 7:1 (2024)
Xin-Yu Chen, Pan Gao, Chu-Dan Qiu, Ya-Nan Lu, Fan Yang, Yuanyuan Zhao, Hang Li, Jiang Zhang, Shijie Wei, Tonghao Xing, Xin-Yu Pan, Dong Ruan, Feihao Zhang, Keren Li, Guilu Long, “A noise-robust quantum dynamics learning protocol based on Choi–Jamiolkowski isomorphism: theory and experiment”, New J. Phys., 26:3 (2024), 033023
Zi-Jian Xu, Jun-Hong An, “Noise mitigation in quantum teleportation”, Phys. Rev. A, 110:1 (2024)
Ilya Vilkoviskiy, Dmitry A. Abanin, “Bound on approximating non-Markovian dynamics by tensor networks in the time domain”, Phys. Rev. B, 109:20 (2024)
Yingwen Wu, Zetong Li, Dafa Zhao, Tian Luan, Xutao Yu, Zaichen Zhang, 2024 5th Information Communication Technologies Conference (ICTC), 2024, 54
Stefano Martina, Stefano Gherardini, Filippo Caruso, “Machine learning classification of non-Markovian noise disturbing quantum dynamics”, Phys. Scr., 98:3 (2023), 035104
Alexey Melnikov, Mohammad Kordzanganeh, Alexander Alodjants, Ray-Kuang Lee, “Quantum machine learning: from physics to software engineering”, Advances in Physics: X, 8:1 (2023)
Emilio Onorati, Tamara Kohler, Toby S. Cubitt, “Fitting quantum noise models to tomography data”, Quantum, 7 (2023), 1197
Hossein T. Dinani, Diego Tancara, Felipe F. Fanchini, Ariel Norambuena, Raul Coto, “Estimating the degree of non-Markovianity using variational quantum circuits”, Quantum Mach. Intell., 5:2 (2023)
Y.F. Zolotarev, I.A. Luchnikov, J.A. López-Saldívar, A.K. Fedorov, E.O. Kiktenko, “Continuous Monitoring for Noisy Intermediate-Scale Quantum Processors”, Phys. Rev. Applied, 19:1 (2023)