Quantum control based on machine learning in an open quantum system

• Published in: Physics letters. A 2020-12, Vol.384 (35), p.126886, Article 126886
• Main Authors: Zeng, Y.X., Shen, J., Hou, S.C., Gebremariam, T., Li, C.
• Format: Article
• Language: English
• Subjects: Neural network; Open quantum system; Quantum control
• ISSN: 0375-9601; 1873-2429
• DOI: 10.1016/j.physleta.2020.126886

•Quantum control, as a modern quantum technology, has played a significant role in quantum information processing.•However, the main challenge is the decoherence effect existed in quantum system.•Therefore, we proposed a robustness quantum control scheme to suppress the decoherence effect and noise in open quantum system.•Our results show that our scheme is effective for suppressing the influence of the surrounding environment. Designing robust control schemes in n-level open quantum system is significant for quantum computation. Here, we investigate two quantum control strategies based on supervised machine learning to suppress the quantum noise in an open quantum system. One is controlling state distance and the other is governing the average of a Hermitian operator. In this process, the dynamics of the system is mapped to a neural network where the control fields correspond to the weights. Besides, the system is transformed into the coherence Bloch space without using superoperator thus the complications are reduced largely. As an example, the two control protocols are demonstrated in a two-level and four-level systems, respectively. By applying these examples, the results show that the state of the system transfers to the target state and the average of a Hermitian operator to its minimum value in a given time despite disturbed by various types of noise.