Experimental Sample-Efficient Quantum State Tomography via Parallel Measurements

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)PRLTAO0031-900710.1103/PhysRevLett.124.100401], 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 treelike 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 six-qubit and nine-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}=531 441 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.

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Publication Details

Journal
Physical Review Letters
Published
2024-10-17
DOI
https://doi.org/10.1103/physrevlett.133.160801
Citations
9
Primary Topic
Quantum Information and Cryptography
Type
article
Field-Weighted Citation Impact
3.13

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article

Experimental Sample-Efficient Quantum State Tomography via Parallel Measurements

Haolan Yuan, Gaogang Xie, Liangyu Che, Chang-Kang Hu et al.
9 citations
Physical Review Letters
Quantum Information and Cryptography
3.13
article

Experimental Sample-Efficient Quantum State Tomography via Parallel Measurements

Haolan Yuan, Gaogang Xie, Liangyu Che, Chang-Kang Hu, Yuxuan Zhou, Guangmin Hu, C. L. Liu, Song Liu, Tao Xin, Ruiyang Zhou, Chao Wei, Dapeng Yu, Dian Tan, Haiyang Qin
article en
9 citations

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)PRLTAO0031-900710.1103/PhysRevLett.124.100401], 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 treelike 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 six-qubit and nine-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}=531 441 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.

Physical Review LettersVol. 133(16)
Southern University of Science and Technology (CN), Hefei University (CN), Quantum (Australia) (AU), Beijing Academy of Quantum Information Sciences (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Science, Technology and Innovation Commission of Shenzhen Municipality, Southern University of Science and Technology, Guangdong Province Key Laboratory of Computational Science, National Key Research and Development Program of China, Special Project for Research and Development in Key areas of Guangdong Province, Shenzhen Science and Technology Innovation Program, Basic and Applied Basic Research Foundation of Guangdong Province
Affordable and clean energy
Openalex Percentile: Top 7%
Quantum Information and Cryptography
3.13
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