Analysing correlated noise on the surface code using adaptive decoding algorithms

Naomi H. Nickerson1 and Benjamin J. Brown2,3

1Quantum Optics and Laser Science, Blackett Laboratory, Imperial College London, Prince Consort Road, London SW7 2AZ, United Kingdom
2Niels Bohr International Academy, Niels Bohr Institute, Blegdamsvej 17, 2100 Copenhagen, Denmark
3Centre for Engineered Quantum Systems, School of Physics, University of Sydney, Sydney, New South Wales 2006, Australia

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Abstract

Laboratory hardware is rapidly progressing towards a state where quantum error-correcting codes can be realised. As such, we must learn how to deal with the complex nature of the noise that may occur in real physical systems. Single qubit Pauli errors are commonly used to study the behaviour of error-correcting codes, but in general we might expect the environment to introduce correlated errors to a system. Given some knowledge of structures that errors commonly take, it may be possible to adapt the error-correction procedure to compensate for this noise, but performing full state tomography on a physical system to analyse this structure quickly becomes impossible as the size increases beyond a few qubits. Here we develop and test new methods to analyse blue a particular class of spatially correlated errors by making use of parametrised families of decoding algorithms. We demonstrate our method numerically using a diffusive noise model. We show that information can be learnt about the parameters of the noise model, and additionally that the logical error rates can be improved. We conclude by discussing how our method could be utilised in a practical setting blue and propose extensions of our work to study more general error models.

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[2] Steven T. Flammia and Joel J. Wallman, "Efficient Estimation of Pauli Channels", ACM Transactions on Quantum Computing 1 1, 1 (2020).

[3] Simon Milz and Kavan Modi, "Quantum Stochastic Processes and Quantum non-Markovian Phenomena", PRX Quantum 2 3, 030201 (2021).

[4] G.A.L. White, F.A. Pollock, L.C.L. Hollenberg, K. Modi, and C.D. Hill, "Non-Markovian Quantum Process Tomography", PRX Quantum 3 2, 020344 (2022).

[5] Aleksander Kubica and Michael Vasmer, "Single-shot quantum error correction with the three-dimensional subsystem toric code", Nature Communications 13 1, 6272 (2022).

[6] J. Pablo Bonilla Ataides, David K. Tuckett, Stephen D. Bartlett, Steven T. Flammia, and Benjamin J. Brown, "The XZZX surface code", Nature Communications 12 1, 2172 (2021).

[7] Utkarsh Azad, Aleksandra Lipińska, Shilpa Mahato, Rijul Sachdeva, Debasmita Bhoumik, and Ritajit Majumdar, "Surface code design for asymmetric error channels", IET Quantum Communication 3 3, 174 (2022).

[8] Jiahao Huang, Min Zhuang, Jungeng Zhou, Yi Shen, and Chaohong Lee, "Quantum Metrology Assisted by Machine Learning", Advanced Quantum Technologies 2300329 (2024).

[9] Matthew Girling, Cristina Cîrstoiu, and David Jennings, "Estimation of correlations and nonseparability in quantum channels via unitarity benchmarking", Physical Review Research 4 2, 023041 (2022).

[10] Bernard Ousmane Sane, Rodney Van Meter, and Michal Hajdusek, 2023 IEEE International Conference on Quantum Computing and Engineering (QCE) 1378 (2023) ISBN:979-8-3503-4323-6.

[11] G. A. L. White, K. Modi, and C. D. Hill, "Filtering Crosstalk from Bath Non-Markovianity via Spacetime Classical Shadows", Physical Review Letters 130 16, 160401 (2023).

[12] Armands Strikis, Simon C. Benjamin, and Benjamin J. Brown, "Quantum Computing is Scalable on a Planar Array of Qubits with Fabrication Defects", Physical Review Applied 19 6, 064081 (2023).

[13] G. A. L. White, C. D. Hill, F. A. Pollock, L. C. L. Hollenberg, and K. Modi, "Demonstration of non-Markovian process characterisation and control on a quantum processor", Nature Communications 11 1, 6301 (2020).

[14] Shilin Huang, Michael Newman, and Kenneth R. Brown, "Fault-tolerant weighted union-find decoding on the toric code", Physical Review A 102 1, 012419 (2020).

[15] Georgia M. Nixon and Benjamin J. Brown, "Correcting Spanning Errors With a Fractal Code", IEEE Transactions on Information Theory 67 7, 4504 (2021).

[16] Michael E. Beverland, Aleksander Kubica, and Krysta M. Svore, "Cost of Universality: A Comparative Study of the Overhead of State Distillation and Code Switching with Color Codes", PRX Quantum 2 2, 020341 (2021).

[17] Theerapat Tansuwannont and Debbie Leung, "Achieving Fault Tolerance on Capped Color Codes with Few Ancillas", PRX Quantum 3 3, 030322 (2022).

[18] Nishad Maskara, Aleksander Kubica, and Tomas Jochym-O'Connor, "Advantages of versatile neural-network decoding for topological codes", Physical Review A 99 5, 052351 (2019).

[19] Thomas Wagner, Hermann Kampermann, Dagmar Bruß, and Martin Kliesch, "Pauli channels can be estimated from syndrome measurements in quantum error correction", Quantum 6, 809 (2022).

[20] Salonik Resch and Ulya R. Karpuzcu, "Benchmarking Quantum Computers and the Impact of Quantum Noise", ACM Computing Surveys 54 7, 1 (2022).

[21] Poulami Das, Christopher A. Pattison, Srilatha Manne, Douglas M. Carmean, Krysta M. Svore, Moinuddin Qureshi, and Nicolas Delfosse, 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA) 259 (2022) ISBN:978-1-6654-2027-3.

[22] Benjamin J. Brown and Dominic J. Williamson, "Parallelized quantum error correction with fracton topological codes", Physical Review Research 2 1, 013303 (2020).

[23] Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, and Nicolai Friis, "Optimizing Quantum Error Correction Codes with Reinforcement Learning", Quantum 3, 215 (2019).

[24] Tobias Hangleiter, Pascal Cerfontaine, and Hendrik Bluhm, "Filter-function formalism and software package to compute quantum processes of gate sequences for classical non-Markovian noise", Physical Review Research 3 4, 043047 (2021).

[25] Qian Xu, Alireza Seif, Haoxiong Yan, Nam Mannucci, Bernard Ousmane Sane, Rodney Van Meter, Andrew N. Cleland, and Liang Jiang, "Distributed Quantum Error Correction for Chip-Level Catastrophic Errors", Physical Review Letters 129 24, 240502 (2022).

[26] David K. Tuckett, Stephen D. Bartlett, Steven T. Flammia, and Benjamin J. Brown, "Fault-Tolerant Thresholds for the Surface Code in Excess of 5% Under Biased Noise", Physical Review Letters 124 13, 130501 (2020).

[27] Uwe von Lüpke, Félix Beaudoin, Leigh M. Norris, Youngkyu Sung, Roni Winik, Jack Y. Qiu, Morten Kjaergaard, David Kim, Jonilyn Yoder, Simon Gustavsson, Lorenza Viola, and William D. Oliver, "Two-Qubit Spectroscopy of Spatiotemporally Correlated Quantum Noise in Superconducting Qubits", PRX Quantum 1 1, 010305 (2020).

[28] S. Varona and M. A. Martin-Delgado, "Determination of the semion code threshold using neural decoders", Physical Review A 102 3, 032411 (2020).

[29] Aleksander Kubica and John Preskill, "Cellular-Automaton Decoders with Provable Thresholds for Topological Codes", Physical Review Letters 123 2, 020501 (2019).

[30] Robin Harper and Steven T. Flammia, "Learning Correlated Noise in a 39-Qubit Quantum Processor", PRX Quantum 4 4, 040311 (2023).

[31] Muyuan Li, Daniel Miller, Michael Newman, Yukai Wu, and Kenneth R. Brown, "2D Compass Codes", Physical Review X 9 2, 021041 (2019).

[32] Benjamin J. Brown, "Conservation Laws and Quantum Error Correction: Toward a Generalized Matching Decoder", IEEE BITS the Information Theory Magazine 2 3, 5 (2022).

[33] Christopher T. Chubb and Steven T. Flammia, "Statistical mechanical models for quantum codes with correlated noise", Annales de l'Institut Henri Poincare D 8 2, 269 (2021).

[34] Christopher T. Chubb, "General tensor network decoding of 2D Pauli codes", arXiv:2101.04125, (2021).

[35] Andrew S. Darmawan and David Poulin, "Linear-time general decoding algorithm for the surface code", Physical Review E 97 5, 051302 (2018).

[36] Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, and Nicolai Friis, "Optimizing Quantum Error Correction Codes with Reinforcement Learning", arXiv:1812.08451, (2018).

[37] Steven T. Flammia and Joel J. Wallman, "Efficient estimation of Pauli channels", arXiv:1907.12976, (2019).

[38] Nishad Maskara, Aleksander Kubica, and Tomas Jochym-O'Connor, "Advantages of versatile neural-network decoding for topological codes", arXiv:1802.08680, (2018).

[39] Gregory A. L. White, Petar Jurcevic, Charles D. Hill, and Kavan Modi, "Unifying non-Markovian characterisation with an efficient and self-consistent framework", arXiv:2312.08454, (2023).

The above citations are from Crossref's cited-by service (last updated successfully 2024-05-01 22:01:25) and SAO/NASA ADS (last updated successfully 2024-05-01 22:01:26). The list may be incomplete as not all publishers provide suitable and complete citation data.