Quantum state estimation based on deep learning

doi:10.1088/1674-1056/ad78d7

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.

Keywords: deep learning quantum state estimation IBM quantum processor

Received: 14 June 2024
Revised: 28 August 2024
Accepted manuscript online: 10 September 2024

PACS:	03.67.-a	(Quantum information)
	03.67.Lx	(Quantum computation architectures and implementations)

Corresponding Authors: Zhiguang Han
E-mail: hanzhiguang@hainanu.edu.cn

Cite this article:

Haowen Xiao(肖皓文) and Zhiguang Han(韩枝光) Quantum state estimation based on deep learning 2024 Chin. Phys. B 33 120307

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