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CutQC: using small Quantum computers for large Quantum circuit evaluations

Published:17 April 2021Publication History

ABSTRACT

Quantum computing (QC) is a new paradigm offering the potential of exponential speedups over classical computing for certain computational problems. Each additional qubit doubles the size of the computational state space available to a QC algorithm. This exponential scaling underlies QC’s power, but today’s Noisy Intermediate-Scale Quantum (NISQ) devices face significant engineering challenges in scalability. The set of quantum circuits that can be reliably run on NISQ devices is limited by their noisy operations and low qubit counts. This paper introduces CutQC, a scalable hybrid computing approach that combines classical computers and quantum computers to enable evaluation of quantum circuits that cannot be run on classical or quantum computers alone. CutQC cuts large quantum circuits into smaller subcircuits, allowing them to be executed on smaller quantum devices. Classical postprocessing can then reconstruct the output of the original circuit. This approach offers significant runtime speedup compared with the only viable current alternative—purely classical simulations—and demonstrates evaluation of quantum circuits that are larger than the limit of QC or classical simulation. Furthermore, in real-system runs, CutQC achieves much higher quantum circuit evaluation fidelity using small prototype quantum computers than the state-of-the-art large NISQ devices achieve. Overall, this hybrid approach allows users to leverage classical and quantum computing resources to evaluate quantum programs far beyond the reach of either one alone.

References

  1. Daniel S Abrams and Seth Lloyd. 1997. Simulation of many-body Fermi systems on a universal quantum computer. Physical Review Letters 79, 13 ( 1997 ), 2586. https://doi.org/10.1103/physrevlett.79.2586Google ScholarGoogle ScholarCross RefCross Ref
  2. Frank Arute, Kunal Arya, Ryan Babbush, Dave Bacon, Joseph C. Bardin, Rami Barends, Rupak Biswas, Sergio Boixo, Fernando G. S. L. Brandao, David A. Buell, Brian Burkett, Yu Chen, Zijun Chen, Ben Chiaro, Roberto Collins, William Courtney, Andrew Dunsworth, Edward Farhi, Brooks Foxen, Austin Fowler, Craig Gidney, Marissa Giustina, Rob Graf, Keith Guerin, Steve Habegger, Matthew P. Harrigan, Michael J. Hartmann, Alan Ho, Markus Hofmann, Trent Huang, Travis S. Humble, Sergei V. Isakov, Evan Jefrey, Zhang Jiang, Dvir Kafri, Kostyantyn Kechedzhi, Julian Kelly, Paul V. Klimov, Sergey Knysh, Alexander Korotkov, Fedor Kostritsa, David Landhuis, Mike Lindmark, Erik Lucero, Dmitry Lyakh, Salvatore Mandrà, Jarrod R. McClean, Matthew McEwen, Anthony Megrant, Xiao Mi, Kristel Michielsen, Masoud Mohseni, Josh Mutus, Ofer Naaman, Matthew Neeley, Charles Neill, Murphy Yuezhen Niu, Eric Ostby, Andre Petukhov, John C. Platt, Chris Quintana, Eleanor G. Riefel, Pedram Roushan, Nicholas C. Rubin, Daniel Sank, Kevin J. Satzinger, Vadim Smelyanskiy, Kevin J. Sung, Matthew D. Trevithick, Amit Vainsencher, Benjamin Villalonga, Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Hartmut Neven, and John M. Martinis. 2019. Quantum supremacy using a programmable superconducting processor. Nature 574, 7779 ( 2019 ), 505-510. https://doi.org/10.1038/s41586-019-1666-5 Google ScholarGoogle ScholarCross RefCross Ref
  3. Behrouz Babaki, Dries Van Daele, Bram Weytjens, and Tias Guns. 2017. A Branch-and-Cut Algorithm for Constrained Graph Clustering. In Data Science Meets Optimization Workshop.Google ScholarGoogle Scholar
  4. Adriano Barenco, Artur Ekert, Kalle-Antti Suominen, and Päivi Törmä. 1996. Approximate quantum Fourier transform and decoherence. Physical Review A 54, 1 ( 1996 ), 139. https://doi.org/10.1103/physreva.54.139 Google ScholarGoogle ScholarCross RefCross Ref
  5. Fergus Barratt, James Dborin, Matthias Bal, Vid Stojevic, Frank Pollmann, and Andrew G Green. 2020. Parallel Quantum Simulation of Large Systems on Small Quantum Computers. arXiv: 2003. 12087 ( 2020 ).Google ScholarGoogle Scholar
  6. Ethan Bernstein and Umesh Vazirani. 1997. Quantum complexity theory. SIAM J. Comput. 26, 5 ( 1997 ), 1411-1473. https://doi.org/10.1137/s0097539796300921 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jacob Biamonte, Peter Wittek, Nicola Pancotti, Patrick Rebentrost, Nathan Wiebe, and Seth Lloyd. 2017. Quantum machine learning. Nature 549, 7671 ( 2017 ), 195-202. https://doi.org/10.1038/nature23474 Google ScholarGoogle ScholarCross RefCross Ref
  8. Sergio Boixo, Sergei V Isakov, Vadim N Smelyanskiy, Ryan Babbush, Nan Ding, Zhang Jiang, Michael J Bremner, John M Martinis, and Hartmut Neven. 2018. Characterizing quantum supremacy in near-term devices. Nature Physics 14, 6 ( 2018 ), 595. https://doi.org/10.1038/s41567-018-0124-x Google ScholarGoogle ScholarCross RefCross Ref
  9. Sergey Bravyi, Graeme Smith, and John A Smolin. 2016. Trading classical and quantum computational resources. Physical Review X 6, 2 ( 2016 ), 021043. https: //doi.org/10.1103/physrevx.6. 021043 Google ScholarGoogle ScholarCross RefCross Ref
  10. Zhao-Yun Chen, Qi Zhou, Cheng Xue, Xia Yang, Guang-Can Guo, and Guo-Ping Guo. 2018. 64-qubit quantum circuit simulation. Science Bulletin 63, 15 ( 2018 ), 964-971. https://doi.org/10.1016/j.scib. 2018. 06.007 Google ScholarGoogle ScholarCross RefCross Ref
  11. James W Cooley and John W Tukey. 1965. An algorithm for the machine calculation of complex Fourier series. Math. Comp. 19, 90 ( 1965 ), 297-301. https://doi.org/10.1090/s0025-5718-1965-0178586-1 Google ScholarGoogle ScholarCross RefCross Ref
  12. AD Corcoles, A Kandala, A Javadi-Abhari, DT McClure, AW Cross, K Temme, PD Nation, M Stefen, and JM Gambetta. 2019. Challenges and Opportunities of Near-Term Quantum Computing Systems. arXiv: 1910. 02894 ( 2019 ).Google ScholarGoogle Scholar
  13. Steven A Cuccaro, Thomas G Draper, Samuel A Kutin, and David Petrie Moulton. 2004. A new quantum ripple-carry addition circuit. arXiv:quant-ph/0410184 ( 2004 ).Google ScholarGoogle Scholar
  14. Google. 2018. Announcing Cirq: An Open Source Framework for NISQ Algorithms. https://ai.googleblog.com/ 2018 /07/announcing-cirq-open-sourceframework.html.Google ScholarGoogle Scholar
  15. TM Graham, M Kwon, B Grinkemeyer, Z Marra, X Jiang, MT Lichtman, Y Sun, M Ebert, and M Safman. 2019. Rydberg-mediated entanglement in a twodimensional neutral atom qubit array. Physical Review Letters 123, 23 ( 2019 ), 230501. https://doi.org/10.1103/physrevlett.123.230501Google ScholarGoogle ScholarCross RefCross Ref
  16. Lov K Grover. 1996. A fast quantum mechanical algorithm for database search. In Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing. 212-219. https://doi.org/10.1145/237814.237866 Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Gurobi Optimization, LLC. 2020. Gurobi Optimizer Reference Manual. http://www.gurobi.comGoogle ScholarGoogle Scholar
  18. Thomas Häner and Damian S Steiger. 2017. 0.5 petabyte simulation of a 45-qubit quantum circuit. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1-10. https://doi.org/ 10.1145/3126908.3126947 Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Jef Heckey, Shruti Patil, Ali JavadiAbhari, Adam Holmes, Daniel Kudrow, Kenneth R Brown, Diana Franklin, Frederic T Chong, and Margaret Martonosi. 2015. Compiler management of communication and parallelism for quantum computation. In Proceedings of the Twentieth International Conference on Architectural Support for Programming Languages and Operating Systems. 445-456. https://doi.org/10.1145/2694344.2694357 Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. IBM. 2019. On ?Quantum Supremacy?. https://www.ibm.com/blogs/research/2019/10/on-quantum-supremacy/.Google ScholarGoogle Scholar
  21. Intel. 2014. Intel® Xeon® Processor E5-2670 v3. https://ark.intel.com/content/www/us/en/ark/products/81709/intel-xeonprocessor-e5-2670-v3-30m-cache-2-30-ghz.html.Google ScholarGoogle Scholar
  22. Abhinav Kandala, Kristan Temme, Antonio D Córcoles, Antonio Mezzacapo, Jerry M Chow, and Jay M Gambetta. 2019. Error mitigation extends the computational reach of a noisy quantum processor. Nature 567, 7749 ( 2019 ), 491. https://doi.org/10.1038/s41586-019-1040-7 Google ScholarGoogle ScholarCross RefCross Ref
  23. PV Klimov, Julian Kelly, Z Chen, Matthew Neeley, Anthony Megrant, Brian Burkett, Rami Barends, Kunal Arya, Ben Chiaro, Yu Chen, et al. 2018. Fluctuations of energy-relaxation times in superconducting qubits. Physical Review Letters 121, 9 ( 2018 ), 090502. https://doi.org/10.1103/PhysRevLett.121.090502 Google ScholarGoogle ScholarCross RefCross Ref
  24. B. P. Lanyon, J. D. Whitfield, G. G. Gillett, M. E. Goggin, M. P. Almeida, I. Kassal, J. D. Biamonte, M. Mohseni, B. J. Powell, M. Barbieri, A. Aspuru-Guzik, and A. G. White. 2010. Towards quantum chemistry on a quantum computer. Nature Chemistry 2, 2 ( 2010 ), 106. https://doi.org/10.1038/nchem.483 Google ScholarGoogle ScholarCross RefCross Ref
  25. Gushu Li, Yufei Ding, and Yuan Xie. 2020. Towards eficient superconducting quantum processor architecture design. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. 1031-1045. https://doi.org/10.1145/3373376.3378500 Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Seth Lloyd, Masoud Mohseni, and Patrick Rebentrost. 2013. Quantum algorithms for supervised and unsupervised machine learning. arXiv:1307.0411 ( 2013 ).Google ScholarGoogle Scholar
  27. Igor L Markov, Aneeqa Fatima, Sergei V Isakov, and Sergio Boixo. 2018. Quantum supremacy is both closer and farther than it appears. arXiv: 1807. 10749 ( 2018 ).Google ScholarGoogle Scholar
  28. Microsoft. 2020. Microsoft Quantum Documentation. https://docs.microsoft.com/en-us/quantum/.Google ScholarGoogle Scholar
  29. Nikolaj Moll, Panagiotis Barkoutsos, Lev S Bishop, Jerry M Chow, Andrew Cross, Daniel J Egger, Stefan Filipp, Andreas Fuhrer, Jay M Gambetta, Marc Ganzhorn, Abhinav Kandala, Antonio Mezzacapo, Peter Müller, Walter Riess, Gian Salis, John Smolin, Ivano Tavernelli, and Kristan Temme. 2018. Quantum optimization using variational algorithms on near-term quantum devices. Quantum Science and Technology 3, 3 ( 2018 ), 030503. https://doi.org/10.1088/2058-9565/aab822 Google ScholarGoogle ScholarCross RefCross Ref
  30. Ashley Montanaro. 2016. Quantum algorithms: An overview. npj Quantum Information 2 ( 2016 ), 15023. https://doi.org/10.1038/npjqi. 2015.23 Google ScholarGoogle ScholarCross RefCross Ref
  31. Prakash Murali, Jonathan M Baker, Ali Javadi-Abhari, Frederic T Chong, and Margaret Martonosi. 2019. Noise-adaptive compiler mappings for noisy intermediatescale quantum computers. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems. 1015-1029. https://doi.org/10.1145/3297858.3304075 Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Prakash Murali, David C McKay, Margaret Martonosi, and Ali Javadi-Abhari. 2020. Software mitigation of crosstalk on noisy intermediate-scale quantum computers. In Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems. 1001-1016. https://doi.org/ 10.1145/3373376.3378477 Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. M.A. Nielsen and I.L. Chuang. 2010. Quantum Computation and Quantum Information (10 ed.). Cambridge University Press.Google ScholarGoogle Scholar
  34. James Ostrowski, Jef Linderoth, Fabrizio Rossi, and Stefano Smriglio. 2009. Orbital branching. Mathematical Programming 126, 1 ( 2009 ), 147-178. https: //doi.org/10.1007/s10107-009-0273-x Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Edwin Pednault, John A Gunnels, Giacomo Nannicini, Lior Horesh, Thomas Magerlein, Edgar Solomonik, Erik W Draeger, Eric T Holland, and Robert Wisnief. 2017. Breaking the 49-qubit barrier in the simulation of quantum circuits. arXiv:1710.05867 ( 2017 ).Google ScholarGoogle Scholar
  36. Tianyi Peng, Aram W Harrow, Maris Ozols, and Xiaodi Wu. 2020. Simulating large quantum circuits on a small quantum computer. Physical Review Letters 125, 15 ( 2020 ), 150504.Google ScholarGoogle ScholarCross RefCross Ref
  37. Charles E Porter and Robert G Thomas. 1956. Fluctuations of nuclear reaction widths. Physical Review 104, 2 ( 1956 ), 483. https://doi.org/10.1103/physrev.104.483 Google ScholarGoogle ScholarCross RefCross Ref
  38. John Preskill. 2018. Quantum Computing in the NISQ era and beyond. Quantum 2 ( 2018 ), 79. https://doi.org/10.22331/q-2018-08-06-79 Google ScholarGoogle ScholarCross RefCross Ref
  39. Rigetti. 2020. Rigetti PyQuil Documentation. https://pyquildocs.rigetti.com/en/stable/.Google ScholarGoogle Scholar
  40. John M. Shalf and Robert Leland. 2015. Computing beyond Moore's Law. Computer 48, 12 ( 2015 ), 14-23. https://doi.org/10.1109/ mc. 2015.374 Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Sarah Sheldon, Easwar Magesan, Jerry M Chow, and Jay M Gambetta. 2016. Procedure for systematically tuning up cross-talk in the cross-resonance gate. Physical Review A 93, 6 ( 2016 ), 060302. https://doi.org/10.1103/physreva.93.060302 Google ScholarGoogle ScholarCross RefCross Ref
  42. Peter W Shor. 1999. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Rev. 41, 2 ( 1999 ), 303-332. https://doi.org/10.1137/s0036144598347011 Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Bochen Tan and Jason Cong. 2020. Optimal layout synthesis for quantum computing. In IEEE/ACM International Conference On Computer Aided Design. IEEE, 1-9.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Bochen Tan and Jason Cong. 2020. Optimality study of existing quantum computing layout synthesis tools. IEEE Trans. Comput. ( 2020 ). https://doi.org/10. 1109/TC. 2020.3009140 Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Swamit S Tannu and Moinuddin K Qureshi. 2019. Ensemble of Diverse Mappings: Improving Reliability of Quantum Computers by Orchestrating Dissimilar Mistakes. In Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture. 253-265. https://doi.org/10.1145/3352460.3358257 Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Swamit S Tannu and Moinuddin K Qureshi. 2019. Mitigating Measurement Errors in Quantum Computers by Exploiting State-Dependent Bias. In Proceedings of the 52nd Annual IEEE/ACM International Symposium on Microarchitecture. 279-290. https://doi.org/10.1145/3352460.3358265 Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Swamit S Tannu and Moinuddin K Qureshi. 2019. Not all qubits are created equal: A case for variability-aware policies for NISQ-era quantum computers. In Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems. 987-999. https: //doi.org/10.1145/3297858.3304007 Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Benjamin Villalonga, Sergio Boixo, Bron Nelson, Christopher Henze, Eleanor Riefel, Rupak Biswas, and Salvatore Mandrà. 2019. A flexible high-performance simulator for verifying and benchmarking quantum circuits implemented on real hardware. npj Quantum Information 5 ( 2019 ), 1-16. https://doi.org/10.1038/ s41534-019-0196-1 Google ScholarGoogle ScholarCross RefCross Ref
  49. Benjamin Villalonga, Dmitry Lyakh, Sergio Boixo, Hartmut Neven, Travis S Humble, Rupak Biswas, Eleanor G Riefel, Alan Ho, and Salvatore Mandrà. 2020. Establishing the quantum supremacy frontier with a 281 pflop/s simulation. Quantum Science and Technology 5, 3 ( 2020 ), 034003. https://doi.org/10.1088/2058-9565/ab7eeb Google ScholarGoogle ScholarCross RefCross Ref
  50. Endong Wang, Qing Zhang, Bo Shen, Guangyong Zhang, Xiaowei Lu, Qing Wu, and Yajuan Wang. 2014. Intel math kernel library. In High-Performance Computing on the Intel® Xeon Phi?. Springer, 167-188. https://doi.org/10.1007/978-3-319-06486-4_7Google ScholarGoogle Scholar
  51. Xin-Chuan Wu, Sheng Di, Emma Maitreyee Dasgupta, Franck Cappello, Hal Finkel, Yuri Alexeev, and Frederic T Chong. 2019. Full-state quantum circuit simulation by using data compression. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. 1-24. https://doi.org/10.1145/3295500.3356155 Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Xiao Yuan, Jinzhao Sun, Junyu Liu, Qi Zhao, and You Zhou. 2020. Quantum simulation with hybrid tensor networks. arXiv:2007. 00958 ( 2020 ).Google ScholarGoogle Scholar

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