Error mitigated shadow estimation based on virtual distillation
Abstract: Shadow estimation is a method for deducing numerous properties of an unknown quantum state through a limited set of measurements, which suffers from noises in quantum devices. In this paper, we introduce an error-mitigated shadow estimation approach based on virtual distillation, tailored for applications in near-term quantum devices. Our methodology leverages the qubit reset technique, thereby reducing the associated qubit overhead. Crucially, our approach ensures that the required qubit resources remain independent of the desired accuracy and avoid an exponential measurement overhead, marking a substantial advancement in practical applications. Furthermore, our technique accommodates a mixed Clifford and Pauli-type shadow, which can result in a reduction in the number of required measurements across various scenarios. We also study the trade-off between circuit depth and measurement overhead quantitatively. Through numerical simulations, we substantiate the efficacy of our error mitigation method, establishing its utility in enhancing the robustness of shadow estimations on near-term quantum devices.
- S. Aaronson, “Shadow tomography of quantum states,” in Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018, pp. 325–338.
- H.-Y. Huang, R. Kueng, and J. Preskill, “Predicting many properties of a quantum system from very few measurements,” Nature Physics, vol. 16, no. 10, pp. 1050–1057, 2020.
- T. Zhang, J. Sun, X.-X. Fang, X.-M. Zhang, X. Yuan, and H. Lu, “Experimental quantum state measurement with classical shadows,” Physical Review Letters, vol. 127, no. 20, p. 200501, 2021.
- A. Elben, R. Kueng, H.-Y. R. Huang, R. van Bijnen, C. Kokail, M. Dalmonte, P. Calabrese, B. Kraus, J. Preskill, P. Zoller, and B. Vermersch, “Mixed-state entanglement from local randomized measurements,” Phys. Rev. Lett., vol. 125, p. 200501, Nov 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.125.200501
- Y. Zhou, P. Zeng, and Z. Liu, “Single-copies estimation of entanglement negativity,” Phys. Rev. Lett., vol. 125, p. 200502, Nov 2020. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.125.200502
- Z. Liu, Y. Tang, H. Dai, P. Liu, S. Chen, and X. Ma, “Detecting entanglement in quantum many-body systems via permutation moments,” Phys. Rev. Lett., vol. 129, p. 260501, Dec 2022. [Online]. Available: https://link.aps.org/doi/10.1103/PhysRevLett.129.260501
- A. Neven, J. Carrasco, V. Vitale, C. Kokail, A. Elben, M. Dalmonte, P. Calabrese, P. Zoller, B. Vermersch, R. Kueng et al., “Symmetry-resolved entanglement detection using partial transpose moments,” npj Quantum Information, vol. 7, no. 1, p. 152, 2021.
- G. Li, Z. Song, and X. Wang, “Vsql: Variational shadow quantum learning for classification,” in Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 9, 2021, pp. 8357–8365.
- G. Struchalin, Y. A. Zagorovskii, E. Kovlakov, S. Straupe, and S. Kulik, “Experimental estimation of quantum state properties from classical shadows,” PRX Quantum, vol. 2, no. 1, p. 010307, 2021.
- S. H. Sack, R. A. Medina, A. A. Michailidis, R. Kueng, and M. Serbyn, “Avoiding barren plateaus using classical shadows,” PRX Quantum, vol. 3, no. 2, p. 020365, 2022.
- C. Hadfield, S. Bravyi, R. Raymond, and A. Mezzacapo, “Measurements of quantum hamiltonians with locally-biased classical shadows,” Communications in Mathematical Physics, vol. 391, no. 3, pp. 951–967, 2022.
- C. Hadfield, “Adaptive pauli shadows for energy estimation,” arXiv preprint arXiv:2105.12207, 2021.
- R. J. Garcia, Y. Zhou, and A. Jaffe, “Quantum scrambling with classical shadows,” Physical Review Research, vol. 3, no. 3, p. 033155, 2021.
- M. McGinley, S. Leontica, S. J. Garratt, J. Jovanovic, and S. H. Simon, “Quantifying information scrambling via classical shadow tomography on programmable quantum simulators,” Physical Review A, vol. 106, no. 1, p. 012441, 2022.
- W. J. Huggins, B. A. O’Gorman, N. C. Rubin, D. R. Reichman, R. Babbush, and J. Lee, “Unbiasing fermionic quantum monte carlo with a quantum computer,” Nature, vol. 603, no. 7901, pp. 416–420, 2022.
- H.-Y. Huang, R. Kueng, G. Torlai, V. V. Albert, and J. Preskill, “Provably efficient machine learning for quantum many-body problems,” Science, vol. 377, no. 6613, p. eabk3333, 2022.
- S. Notarnicola, A. Elben, T. Lahaye, A. Browaeys, S. Montangero, and B. Vermersch, “A randomized measurement toolbox for an interacting rydberg-atom quantum simulator,” New Journal of Physics, vol. 25, no. 10, p. 103006, 2023.
- J. Helsen, M. Ioannou, J. Kitzinger, E. Onorati, A. Werner, J. Eisert, and I. Roth, “Shadow estimation of gate-set properties from random sequences,” Nature Communications, vol. 14, no. 1, p. 5039, 2023.
- B. Wu, J. Sun, Q. Huang, and X. Yuan, “Overlapped grouping measurement: A unified framework for measuring quantum states,” Quantum, vol. 7, p. 896, 2023.
- R. Yang, T. Wang, B.-N. Lu, Y. Li, and X. Xu, “Shadow-based quantum subspace algorithm for the nuclear shell model,” arXiv preprint arXiv:2306.08885, 2023.
- K. Wan, W. J. Huggins, J. Lee, and R. Babbush, “Matchgate shadows for fermionic quantum simulation,” Communications in Mathematical Physics, pp. 1–72, 2023.
- H.-Y. Liu, T.-P. Sun, Y.-C. Wu, and G.-P. Guo, “Variational quantum algorithms for the steady states of open quantum systems,” Chinese Physics Letters, vol. 38, no. 8, p. 080301, 2021.
- H. Zhou, R. Mao, and X. Sun, “Hybrid algorithm simulating non-equilibrium steady states of an open quantum system,” arXiv preprint arXiv:2309.06665, 2023.
- A. Rath, C. Branciard, A. Minguzzi, and B. Vermersch, “Quantum fisher information from randomized measurements,” Physical Review Letters, vol. 127, no. 26, p. 260501, 2021.
- L. Chirolli and G. Burkard, “Decoherence in solid-state qubits,” Advances in Physics, vol. 57, no. 3, pp. 225–285, 2008.
- M. H. Devoret, A. Wallraff, and J. M. Martinis, “Superconducting qubits: A short review,” arXiv preprint cond-mat/0411174, 2004.
- C. D. Bruzewicz, J. Chiaverini, R. McConnell, and J. M. Sage, “Trapped-ion quantum computing: Progress and challenges,” Applied Physics Reviews, vol. 6, no. 2, 2019.
- J. Preskill, “Quantum computing in the nisq era and beyond,” Quantum, vol. 2, p. 79, 2018.
- S. Chen, W. Yu, P. Zeng, and S. T. Flammia, “Robust shadow estimation,” PRX Quantum, vol. 2, no. 3, p. 030348, 2021.
- D. E. Koh and S. Grewal, “Classical shadows with noise,” Quantum, vol. 6, p. 776, 2022.
- H. Jnane, J. Steinberg, Z. Cai, H. C. Nguyen, and B. Koczor, “Quantum error mitigated classical shadows,” 2023.
- W. J. Huggins, S. McArdle, T. E. O’Brien, J. Lee, N. C. Rubin, S. Boixo, K. B. Whaley, R. Babbush, and J. R. McClean, “Virtual distillation for quantum error mitigation,” Physical Review X, vol. 11, no. 4, p. 041036, 2021.
- B. Koczor, “Exponential error suppression for near-term quantum devices,” Physical Review X, vol. 11, no. 3, p. 031057, 2021.
- ——, “The dominant eigenvector of a noisy quantum state,” New Journal of Physics, vol. 23, no. 12, p. 123047, 2021.
- Z. Cai, X. Xu, and S. C. Benjamin, “Mitigating coherent noise using pauli conjugation,” npj Quantum Information, vol. 6, no. 1, p. 17, 2020.
- J. J. Wallman and J. Emerson, “Noise tailoring for scalable quantum computation via randomized compiling,” Physical Review A, vol. 94, no. 5, p. 052325, 2016.
- A. Hashim, R. K. Naik, A. Morvan, J.-L. Ville, B. Mitchell, J. M. Kreikebaum, M. Davis, E. Smith, C. Iancu, K. P. O’Brien et al., “Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor,” arXiv preprint arXiv:2010.00215, 2020.
- F. A. Bovino, G. Castagnoli, A. Ekert, P. Horodecki, C. M. Alves, and A. V. Sergienko, “Direct measurement of nonlinear properties of bipartite quantum states,” Physical review letters, vol. 95, no. 24, p. 240407, 2005.
- P. Horodecki, “Measuring quantum entanglement without prior state reconstruction,” Physical review letters, vol. 90, no. 16, p. 167901, 2003.
- A. K. Ekert, C. M. Alves, D. K. Oi, M. Horodecki, P. Horodecki, and L. C. Kwek, “Direct estimations of linear and nonlinear functionals of a quantum state,” Physical review letters, vol. 88, no. 21, p. 217901, 2002.
- Y. Zhou and Z. Liu, “A hybrid framework for estimating nonlinear functions of quantum states,” arXiv preprint arXiv:2208.08416, 2022.
- A. Seif, Z.-P. Cian, S. Zhou, S. Chen, and L. Jiang, “Shadow distillation: Quantum error mitigation with classical shadows for near-term quantum processors,” PRX Quantum, vol. 4, no. 1, p. 010303, 2023.
- J. Yirka and Y. Subaşı, “Qubit-efficient entanglement spectroscopy using qubit resets,” Quantum, vol. 5, p. 535, 2021.
- C. O. Marrero, M. Kieferová, and N. Wiebe, “Entanglement-induced barren plateaus,” PRX Quantum, vol. 2, no. 4, p. 040316, 2021.
- R. O’Donnell and J. Wright, “Efficient quantum tomography,” in Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, 2016, pp. 899–912.
- H.-Y. Huang, R. Kueng, and J. Preskill, “Efficient estimation of pauli observables by derandomization,” Physical review letters, vol. 127, no. 3, p. 030503, 2021.
- A. Acharya, S. Saha, and A. M. Sengupta, “Shadow tomography based on informationally complete positive operator-valued measure,” Physical Review A, vol. 104, no. 5, p. 052418, 2021.
- H.-Y. Hu, S. Choi, and Y.-Z. You, “Classical shadow tomography with locally scrambled quantum dynamics,” Physical Review Research, vol. 5, no. 2, p. 023027, 2023.
- H.-Y. Hu and Y.-Z. You, “Hamiltonian-driven shadow tomography of quantum states,” Physical Review Research, vol. 4, no. 1, p. 013054, 2022.
- A. Zhao, N. C. Rubin, and A. Miyake, “Fermionic partial tomography via classical shadows,” Physical Review Letters, vol. 127, no. 11, p. 110504, 2021.
- J. M. Lukens, K. J. Law, and R. S. Bennink, “A bayesian analysis of classical shadows,” npj Quantum Information, vol. 7, no. 1, p. 113, 2021.
- G. H. Low, “Classical shadows of fermions with particle number symmetry,” arXiv preprint arXiv:2208.08964, 2022.
- M. Ippoliti, Y. Li, T. Rakovszky, and V. Khemani, “Operator relaxation and the optimal depth of classical shadows,” Physical Review Letters, vol. 130, no. 23, p. 230403, 2023.
- T. Gu, X. Yuan, and B. Wu, “Efficient measurement schemes for bosonic systems,” Quantum Science and Technology, vol. 8, no. 4, p. 045008, 2023.
- B. O’Gorman, “Fermionic tomography and learning,” arXiv preprint arXiv:2207.14787, 2022.
- C. Bertoni, J. Haferkamp, M. Hinsche, M. Ioannou, J. Eisert, and H. Pashayan, “Shallow shadows: Expectation estimation using low-depth random clifford circuits,” arXiv preprint arXiv:2209.12924, 2022.
- S. Shivam, C. W. von Keyserlingk, and S. L. Sondhi, “On classical and hybrid shadows of quantum states,” SciPost Physics, vol. 14, no. 5, p. 094, 2023.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.