Quantum Advantage with Shallow Circuits Under Arbitrary Corruption

Authors Atsuya Hasegawa, François Le Gall



PDF
Thumbnail PDF

File

LIPIcs.ISAAC.2021.74.pdf
  • Filesize: 1.08 MB
  • 16 pages

Document Identifiers

Author Details

Atsuya Hasegawa
  • The University of Tokyo, Japan
François Le Gall
  • Nagoya University, Japan

Acknowledgements

AH is grateful to Hidefumi Hiraishi and Hiroshi Imai for helpful discussions and continuous supports. FLG would also like to thank Robert König for useful discussions.

Cite As Get BibTex

Atsuya Hasegawa and François Le Gall. Quantum Advantage with Shallow Circuits Under Arbitrary Corruption. In 32nd International Symposium on Algorithms and Computation (ISAAC 2021). Leibniz International Proceedings in Informatics (LIPIcs), Volume 212, pp. 74:1-74:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021) https://doi.org/10.4230/LIPIcs.ISAAC.2021.74

Abstract

Recent works by Bravyi, Gosset and König (Science 2018), Bene Watts et al. (STOC 2019), Coudron, Stark and Vidick (QIP 2019) and Le Gall (CCC 2019) have shown unconditional separations between the computational powers of shallow (i.e., small-depth) quantum and classical circuits: quantum circuits can solve in constant depth computational problems that require logarithmic depth to solve with classical circuits. Using quantum error correction, Bravyi, Gosset, König and Tomamichel (Nature Physics 2020) further proved that a similar separation still persists even if quantum circuits are subject to local stochastic noise.
In this paper, we consider the case where any constant fraction of the qubits (for instance, huge blocks of qubits) may be arbitrarily corrupted at the end of the computation. We make a first step forward towards establishing a quantum advantage even in this extremely challenging setting: we show that there exists a computational problem that can be solved in constant depth by a quantum circuit but such that even solving any large subproblem of this problem requires logarithmic depth with bounded fan-in classical circuits. This gives another compelling evidence of the computational power of quantum shallow circuits. 
In order to show our result, we consider the Graph State Sampling problem (which was also used in prior works) on expander graphs. We exploit the "robustness" of expander graphs against vertex corruption to show that a subproblem hard for small-depth classical circuits can still be extracted from the output of the corrupted quantum circuit.

Subject Classification

ACM Subject Classification
  • Theory of computation
Keywords
  • Quantum computing
  • circuit complexity
  • constant-depth circuits

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Scott Aaronson and Alex Arkhipov. The computational complexity of linear optics. In Proceedings of the 43rd Annual ACM SIGACT Symposium on Theory of Computing (STOC 2011), pages 333-342, 2011. Google Scholar
  2. Scott Aaronson and Alex Arkhipov. Bosonsampling is far from uniform. Quantum Information & Computation, 14(15–16):1383-1423, 2014. Google Scholar
  3. Scott Aaronson and Lijie Chen. Complexity-Theoretic Foundations of Quantum Supremacy Experiments. In Proceedings of 32nd Computational Complexity Conference (CCC 2017), volume 79 of LIPIcs, pages 22:1-22:67, 2017. Google Scholar
  4. Andris Ambainis. Understanding quantum algorithm via query complexity. In Proceedings of the International Congress of Mathematicians (ICM 2018), pages 3265-3285, 2019. Google Scholar
  5. Matthew Amy, Dmitri Maslov, Michele Mosca, and Martin Roetteler. A meet-in-the-middle algorithm for fast synthesis of depth-optimal quantum circuits. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 32(6):818-830, 2013. Google Scholar
  6. Frank Arute et al. Quantum supremacy using a programmable superconducting processor. Nature, 574(7779):505-510, 2019. Google Scholar
  7. Jonathan Barrett, Carlton M. Caves, Bryan Eastin, Matthew B. Elliott, and Stefano Pironio. Modeling pauli measurements on graph states with nearest-neighbor classical communication. Physical Review A, 75(1):012103, 2007. Google Scholar
  8. Ya. M. Barzdin. On the realization of networks in three-dimensional space. In Selected Works of A. N. Kolmogorov: Volume III: Information Theory and the Theory of Algorithms, pages 194-202. Springer, 1993. Google Scholar
  9. Adam Bene Watts, Robin Kothari, Luke Schaeffer, and Avishay Tal. Exponential separation between shallow quantum circuits and unbounded fan-in shallow classical circuits. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (STOC 2019), pages 515-526, 2019. Google Scholar
  10. Jan Benhelm, Gerhard Kirchmair, Christian F. Roos, and Rainer Blatt. Towards fault-tolerant quantum computing with trapped ions. Nature Physics, 4(6):463-466, 2008. Google Scholar
  11. Paul Benioff. The computer as a physical system: A microscopic quantum mechanical hamiltonian model of computers as represented by turing machines. Journal of Statistical Physics, 22(5):563-591, 1980. Google Scholar
  12. Ethan Bernstein and Umesh Vazirani. Quantum complexity theory. SIAM Journal on Computing, 26(5):1411-1473, 1997. Google Scholar
  13. Adam Bouland, Bill Fefferman, Chinmay Nirkhe, and Umesh Vazirani. "Quantum Supremacy" and the Complexity of Random Circuit Sampling. In Proceedings of 10th Innovations in Theoretical Computer Science Conference (ITCS 2019), volume 124 of LIPIcs, pages 15:1-15:2, 2019. Google Scholar
  14. Sergey Bravyi, David Gosset, and Robert König. Quantum advantage with shallow circuits. Science, 362(6412):308-311, 2018. Google Scholar
  15. Sergey Bravyi, David Gosset, Robert König, and Marco Tomamichel. Quantum advantage with noisy shallow circuits. Nature Physics, 16(10):1040-1045, 2020. Google Scholar
  16. Michael J. Bremner, Richard Jozsa, and Dan J. Shepherd. Classical simulation of commuting quantum computations implies collapse of the polynomial hierarchy. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 467(2126):459-472, 2011. Google Scholar
  17. Michael J. Bremner, Ashley Montanaro, and Dan J. Shepherd. Average-case complexity versus approximate simulation of commuting quantum computations. Physical Review Letters, 117(8):080501, 2016. Google Scholar
  18. Michael J. Bremner, Ashley Montanaro, and Dan J. Shepherd. Achieving quantum supremacy with sparse and noisy commuting quantum computations. Quantum, 1:8, 2017. Google Scholar
  19. Michael Capalbo, Omer Reingold, Salil Vadhan, and Avi Wigderson. Randomness conductors and constant-degree lossless expanders. In Proceedings of the 34th Annual ACM SIGACT Symposium on Theory of Computing (STOC 2002), pages 659-668, 2002. Google Scholar
  20. Julia Chuzhoy and Rachit Nimavat. Large minors in expanders. arXiv, 2019. URL: http://arxiv.org/abs/1901.09349.
  21. Matthew Coudron, Jalex Stark, and Thomas Vidick. Trading locality for time: certifiable randomness from low-depth circuits. Communications in Mathematical Physics, 382(1):49-86, 2021. Also presented at QIP 2019. Google Scholar
  22. Ronald De Wolf. Quantum communication and complexity. Theoretical Computer Science, 287(1):337-353, 2002. Google Scholar
  23. Reinhard Diestel. Graph theory, volume 173 of Graduate texts in mathematics. Springer, 2000. Google Scholar
  24. Andreas Wallraff et al. Strong coupling of a single photon to a superconducting qubit using circuit quantum electrodynamics. Nature, 431(7005):162-167, 2004. Google Scholar
  25. Richard P Feynman. Simulating physics with computers. Int. J. Theor. Phys, 21(6/7):467-488, 1982. Google Scholar
  26. François Le Gall. Average-Case Quantum Advantage with Shallow Circuits. In Proceedings of 34th Computational Complexity Conference (CCC 2019), volume 137 of LIPIcs, pages 21:1-21:20, 2019. Google Scholar
  27. Daniel Grier, Nathan Ju, and Luke Schaeffer. Interactive quantum advantage with noisy, shallow clifford circuits. arXiv, 2021. URL: http://arxiv.org/abs/2102.06833.
  28. Daniel Grier and Luke Schaeffer. Interactive shallow clifford circuits: quantum advantage against NC¹ and beyond. In Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing (STOC 2020), pages 875-888, 2020. Google Scholar
  29. Misha Gromov and Larry Guth. Generalizations of the kolmogorov-barzdin embedding estimates. Duke Mathematical Journal, 161(13):2549-2603, 2012. Google Scholar
  30. Marc Hein, Jens Eisert, and Hans J. Briegel. Multiparty entanglement in graph states. Physical Review A, 69(6):062311, 2004. Google Scholar
  31. Emmanuel Kowalski. An introduction to expander graphs. Société Mathématique de France, 2019. Google Scholar
  32. Michael Krivelevich. Expanders - how to find them, and what to find in them. Surveys in Combinatorics, pages 115-142, 2019. Google Scholar
  33. Michael Krivelevich and Benjamin Sudakov. Minors in expanding graphs. Geometric and Functional Analysis, 19(1):294-331, 2009. Google Scholar
  34. Hoi-Kwong Lo. Classical-communication cost in distributed quantum-information processing: a generalization of quantum-communication complexity. Physical Review A, 62(1):012313, 2000. Google Scholar
  35. N. David Mermin. Extreme quantum entanglement in a superposition of macroscopically distinct states. Physical Review Letters, 65(15):1838, 1990. Google Scholar
  36. Yasunobu Nakamura, Chii Dong Chen, and Jaw Shen Tsai. Spectroscopy of energy-level splitting between two macroscopic quantum states of charge coherently superposed by josephson coupling. Physical Review Letters, 79(12):2328, 1997. Google Scholar
  37. Michael A. Nielsen and Isaac L. Chuang. Quantum Computation and Quantum Information. Cambridge University Press, 2000. Google Scholar
  38. Jeremy L O'Brien. Optical quantum computing. Science, 318(5856):1567-1570, 2007. Google Scholar
  39. Asher Peres. Incompatible results of quantum measurements. Physics Letters A, 151(3-4):107-108, 1990. Google Scholar
  40. John Preskill. Quantum computing and the entanglement frontier. Bulletin of the American Physical Society, 58, 2013. Google Scholar
  41. John Preskill. Quantum computing in the NISQ era and beyond. Quantum, 2:79, 2018. Google Scholar
  42. Ran Raz and Avishay Tal. Oracle separation of BQP and PH. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (STOC 2019), pages 13-23, 2019. Google Scholar
  43. Peter W. Shor. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing, 26(5):1484-1509, 1997. Google Scholar
  44. Barbara M. Terhal and David P. DiVincenzo. Adptive quantum computation, constant depth quantum circuits and arthur-merlin games. Quantum Information & Computation, 4(2):134-145, 2004. Google Scholar
  45. Jian-Qiang You and Franco Nori. Atomic physics and quantum optics using superconducting circuits. Nature, 474(7353):589-597, 2011. Google Scholar
  46. Han-Sen Zhong et al. Quantum computational advantage using photons. Science, 370:1460-1463, 2020. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail