An Improved Sample Complexity Lower Bound for (Fidelity) Quantum State Tomography

Henry Yuen

Columbia University

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Abstract

We show that $\Omega(rd/\epsilon)$ copies of an unknown rank-$r$, dimension-$d$ quantum mixed state are necessary in order to learn a classical description with $1 – \epsilon$ fidelity. This improves upon the tomography lower bounds obtained by Haah, et al. and Wright (when closeness is measured with respect to the fidelity function).

This paper presents a sharper lower bound on the number of copies of a quantum state needed to learn a classical description of it.

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► References

[1] Dagmar Bruß and Chiara Macchiavello. Optimal state estimation for $d$-dimensional quantum systems. Physics Letters A, 253 (5-6): 249–251, 1999. https:/​/​doi.org/​10.1016/​S0375-9601(99)00099-7.
https:/​/​doi.org/​10.1016/​S0375-9601(99)00099-7

[2] Jeongwan Haah, Aram W Harrow, Zhengfeng Ji, Xiaodi Wu, and Nengkun Yu. Sample-optimal tomography of quantum states. IEEE Transactions on Information Theory, 63 (9): 5628–5641, 2017. https:/​/​doi.org/​10.1145/​2897518.2897585.
https:/​/​doi.org/​10.1145/​2897518.2897585

[3] Michael Keyl and Reinhard F Werner. Optimal cloning of pure states, testing single clones. Journal of Mathematical Physics, 40 (7): 3283–3299, 1999. https:/​/​doi.org/​10.1063/​1.532887.
https:/​/​doi.org/​10.1063/​1.532887

[4] Ryan O'Donnell and John Wright. Efficient quantum tomography. In Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, pages 899–912, 2016. https:/​/​doi.org/​10.1145/​2897518.2897544.
https:/​/​doi.org/​10.1145/​2897518.2897544

[5] Reinhard F Werner. Optimal cloning of pure states. Physical Review A, 58 (3): 1827, 1998. https:/​/​doi.org/​10.1103/​PhysRevA.58.1827.
https:/​/​doi.org/​10.1103/​PhysRevA.58.1827

[6] Andreas Winter. Coding theorem and strong converse for quantum channels. IEEE Transactions on Information Theory, 45 (7): 2481–2485, 1999. https:/​/​doi.org/​10.1109/​18.796385.
https:/​/​doi.org/​10.1109/​18.796385

[7] John Wright. How to learn a quantum state. PhD thesis, Carnegie Mellon University, 2016.

Cited by

[1] Anurag Anshu and Srinivasan Arunachalam, "A survey on the complexity of learning quantum states", Nature Reviews Physics 6 1, 59 (2023).

[2] Xinbiao Wang, Yuxuan Du, Zhuozhuo Tu, Yong Luo, Xiao Yuan, and Dacheng Tao, "Transition role of entangled data in quantum machine learning", Nature Communications 15 1, 3716 (2024).

[3] Rafael Wagner, Filipa C R Peres, Emmanuel Zambrini Cruzeiro, and Ernesto F Galvão, "Unitary-invariant method for witnessing nonstabilizerness in quantum processors", Journal of Physics A: Mathematical and Theoretical 58 28, 285302 (2025).

[4] Yanglin Hu, Enrique Cervero-Martín, Elias Theil, Laura Mančinska, and Marco Tomamichel, "Sample-optimal and memory-efficient quantum state tomography", Physical Review A 113 5, 052446 (2026).

[5] Harshdeep Singh, Sonjoy Majumder, and Sabyashachi Mishra, "Hückel molecular orbital theory on a quantum computer: A scalable system-agnostic variational implementation with compact encoding", The Journal of Chemical Physics 160 19, 194106 (2024).

[6] Angelos Pelecanos, Jack Spilecki, and John Wright, Proceedings of the 58th Annual ACM Symposium on Theory of Computing 1266 (2026) ISBN:9798400725364.

[7] Subhadeep Mondal and Amit Kumar Dutta, "A modified least squares-based tomography with density matrix perturbation and linear entropy consideration along with performance analysis", New Journal of Physics 25 8, 083051 (2023).

[8] Sreejith Sreekumar and Mario Berta, 2024 IEEE International Symposium on Information Theory (ISIT) 333 (2024) ISBN:979-8-3503-8284-6.

[9] Ziv Goldfeld, Dhrumil Patel, Sreejith Sreekumar, and Mark M. Wilde, "Quantum neural estimation of entropies", Physical Review A 109 3, 032431 (2024).

[10] Haimeng Zhao, Laura Lewis, Ishaan Kannan, Yihui Quek, Hsin-Yuan Huang, and Matthias C. Caro, "Learning Quantum States and Unitaries of Bounded Gate Complexity", PRX Quantum 5 4, 040306 (2024).

[11] Sreejith Sreekumar and Mario Berta, "Limit Distribution Theory for Quantum Divergences", IEEE Transactions on Information Theory 71 1, 459 (2025).

[12] Sreejith Sreekumar, Ziv Goldfeld, and Mark M. Wilde, "Performance Guarantees for Quantum Neural Estimation of Entropies", Quantum 10, 2113 (2026).

[13] Federico Girotti, Alfred Godley, and Mădălin Guţă, "Optimal estimation of pure states with displaced-null measurements", Journal of Physics A: Mathematical and Theoretical 57 24, 245304 (2024).

[14] Jayadev Acharya, Abhilash Dharmavarapu, Yuhan Liu, and Nengkun Yu, Proceedings of the 57th Annual ACM Symposium on Theory of Computing 718 (2025) ISBN:9798400715105.

[15] Refik Mansuroglu, Arsalan Adil, Michael J. Hartmann, Zoë Holmes, and Andrew T. Sornborger, "Quantum Tensor-Product Decomposition from Choi-State Tomography", PRX Quantum 5 3, 030306 (2024).

[16] Alexander Mandl, Johanna Barzen, Marvin Bechtold, and Frank Leymann, Communications in Computer and Information Science 2221, 107 (2025) ISBN:978-3-031-72577-7.

[17] Steven T. Flammia and Ryan O'Donnell, "Quantum chi-squared tomography and mutual information testing", Quantum 8, 1381 (2024).

[18] Rafael Wagner, Zohar Schwartzman-Nowik, Ismael L Paiva, Amit Te’eni, Antonio Ruiz-Molero, Rui Soares Barbosa, Eliahu Cohen, and Ernesto F Galvão, "Quantum circuits for measuring weak values, Kirkwood–Dirac quasiprobability distributions, and state spectra", Quantum Science and Technology 9 1, 015030 (2024).

[19] Haimeng Zhao, Giuseppe Carleo, and Filippo Vicentini, "Empirical Sample Complexity of Neural Network Mixed State Reconstruction", Quantum 8, 1358 (2024).

[20] Xiaozhou Feng, Zihan Cheng, and Matteo Ippoliti, "Hardness of Observing Strong-to-Weak Symmetry Breaking", Physical Review Letters 135 20, 200402 (2025).

[21] Yi Shen and Lin Chen, "Additivity of states uniquely determined by marginals", Physical Review A 108 6, 062418 (2023).

[22] Ming-Chien Hsu, En-Jui Kuo, Wei-Hsuan Yu, Jian-Feng Cai, and Min-Hsiu Hsieh, "Quantum State Tomography via Nonconvex Riemannian Gradient Descent", Physical Review Letters 132 24, 240804 (2024).

[23] Jiaqi Leng and Bin Shi, Communications in Computer and Information Science 2724, 170 (2026) ISBN:978-981-95-7828-3.

[24] Alexander M. Dalzell, Sam McArdle, Mario Berta, Przemyslaw Bienias, Chi-Fang Chen, András Gilyén, Connor T. Hann, Michael J. Kastoryano, Emil T. Khabiboulline, Aleksander Kubica, Grant Salton, Samson Wang, and Fernando G. S. L. Brandão, "Quantum algorithms: A survey of applications and end-to-end complexities", arXiv:2310.03011, (2023).

[25] Nic Ezzell, Elliott M. Ball, Aliza U. Siddiqui, Mark M. Wilde, Andrew T. Sornborger, Patrick J. Coles, and Zoë Holmes, "Quantum mixed state compiling", Quantum Science and Technology 8 3, 035001 (2023).

[26] Srinivasan Arunachalam, Sergey Bravyi, Arkopal Dutt, and Theodore J. Yoder, "Optimal algorithms for learning quantum phase states", arXiv:2208.07851, (2022).

[27] Joran van Apeldoorn, Arjan Cornelissen, András Gilyén, and Giacomo Nannicini, "Quantum tomography using state-preparation unitaries", arXiv:2207.08800, (2022).

[28] Rafael Wagner, "Coherence and contextuality as quantum resources", arXiv:2511.16785, (2025).

[29] Nai-Hui Chia, Daniel Liang, and Fang Song, "Quantum State Learning Implies Circuit Lower Bounds", arXiv:2405.10242, (2024).

[30] Jian-Feng Cai, Yuling Jiao, Yinan Li, Xiliang Lu, Jerry Zhijian Yang, and Juntao You, "Online Quantum State Tomography via Stochastic Gradient Descent", arXiv:2507.07601, (2025).

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