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Quantum Physics

arXiv:2409.12614 (quant-ph)
[Submitted on 19 Sep 2024]

Title:Experimental sample-efficient quantum state tomography via parallel measurements

Authors:Chang-Kang Hu, Chao Wei, Chilong Liu, Liangyu Che, Yuxuan Zhou, Guixu Xie, Haiyang Qin, Guantian Hu, Haolan Yuan, Ruiyang Zhou, Song Liu, Dian Tan, Tao Xin, Dapeng Yu
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Abstract:Quantum state tomography (QST) via local measurements on reduced density matrices (LQST) is a promising approach but becomes impractical for large systems. To tackle this challenge, we developed an efficient quantum state tomography method inspired by quantum overlapping tomography [Phys. Rev. Lett. 124, 100401(2020)], which utilizes parallel measurements (PQST). In contrast to LQST, PQST significantly reduces the number of measurements and offers more robustness against shot noise. Experimentally, we demonstrate the feasibility of PQST in a tree-like superconducting qubit chip by designing high-efficiency circuits, preparing W states, ground states of Hamiltonians and random states, and then reconstructing these density matrices using full quantum state tomography (FQST), LQST, and PQST. Our results show that PQST reduces measurement cost, achieving fidelities of 98.68\% and 95.07\% after measuring 75 and 99 observables for 6-qubit and 9-qubit W states, respectively. Furthermore, the reconstruction of the largest density matrix of the 12-qubit W state is achieved with the similarity of 89.23\% after just measuring $243$ parallel observables, while $3^{12}=531441$ complete observables are needed for FQST. Consequently, PQST will be a useful tool for future tasks such as the reconstruction, characterization, benchmarking, and properties learning of states.
Comments: To appear in PRL(2024)
Subjects: Quantum Physics (quant-ph)
Cite as: arXiv:2409.12614 [quant-ph]
  (or arXiv:2409.12614v1 [quant-ph] for this version)
  https://doi.org/10.48550/arXiv.2409.12614
arXiv-issued DOI via DataCite
Journal reference: Phys. Rev. Lett. 133, 160801 (2024)
Related DOI: https://doi.org/10.1103/PhysRevLett.133.160801
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Submission history

From: Dian Tan [view email]
[v1] Thu, 19 Sep 2024 09:34:50 UTC (8,950 KB)
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