Quantum state estimation based on deep learning

doi:10.1088/1674-1056/ad78d7

中国物理B ›› 2024, Vol. 33 ›› Issue (12): 120307-120307.doi: 10.1088/1674-1056/ad78d7

Quantum state estimation based on deep learning

Haowen Xiao(肖皓文) and Zhiguang Han(韩枝光)†

School of Information and Communication Engineering, Hainan University, Haikou 570228, China

收稿日期:2024-06-14 修回日期:2024-08-28 接受日期:2024-09-10 发布日期:2024-11-12
通讯作者: Zhiguang Han E-mail:hanzhiguang@hainanu.edu.cn

Quantum state estimation based on deep learning

Haowen Xiao(肖皓文) and Zhiguang Han(韩枝光)†

School of Information and Communication Engineering, Hainan University, Haikou 570228, China

Received:2024-06-14 Revised:2024-08-28 Accepted:2024-09-10 Published:2024-11-12
Contact: Zhiguang Han E-mail:hanzhiguang@hainanu.edu.cn

摘要/Abstract

摘要： We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements. Through simulations, we have demonstrated that our approach generates functionally equivalent reconstructed states for a wide range of pure and mixed input states. Compared with traditional methods, our system offers the advantage of faster speed. Additionally, by training our system with measurement results containing simulated noise sources, the system shows a significant improvement in average fidelity compared with typical reconstruction methods. We also found that constraining the variational manifold to physical states, i.e., positive semi-definite density matrices, greatly enhances the quality of the reconstructed states in the presence of experimental imperfections and noise. Finally, we validated the correctness and superiority of our model by using data generated on IBM Quantum Platform, a real quantum computer.

关键词: deep learning, quantum state estimation, IBM quantum processor

Abstract: We used deep learning techniques to construct various models for reconstructing quantum states from a given set of coincidence measurements. Through simulations, we have demonstrated that our approach generates functionally equivalent reconstructed states for a wide range of pure and mixed input states. Compared with traditional methods, our system offers the advantage of faster speed. Additionally, by training our system with measurement results containing simulated noise sources, the system shows a significant improvement in average fidelity compared with typical reconstruction methods. We also found that constraining the variational manifold to physical states, i.e., positive semi-definite density matrices, greatly enhances the quality of the reconstructed states in the presence of experimental imperfections and noise. Finally, we validated the correctness and superiority of our model by using data generated on IBM Quantum Platform, a real quantum computer.

Key words: deep learning, quantum state estimation, IBM quantum processor

中图分类号: (Quantum information)

03.67.-a

03.67.Lx (Quantum computation architectures and implementations)

Haowen Xiao(肖皓文) and Zhiguang Han(韩枝光). Quantum state estimation based on deep learning[J]. 中国物理B, 2024, 33(12): 120307-120307.

Haowen Xiao(肖皓文) and Zhiguang Han(韩枝光). Quantum state estimation based on deep learning[J]. Chin. Phys. B, 2024, 33(12): 120307-120307.

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Quantum state estimation based on deep learning

Quantum state estimation based on deep learning

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