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  • Letter
  • Open Access

Simulating time evolution on distributed quantum computers

Finn Lasse Buessen1,2, Dvira Segal2,3, and Ilia Khait1

  • 1Entangled Networks Ltd., Toronto, Ontario M4R 2E4, Canada
  • 2Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
  • 3Department of Chemistry and Centre for Quantum Information and Quantum Control, University of Toronto, Toronto, Ontario M5S 3H6, Canada

Phys. Rev. Research 5, L022003 – Published 4 April, 2023

DOI: https://doi.org/10.1103/PhysRevResearch.5.L022003

Abstract

We study a variation of the Trotter-Suzuki decomposition, in which a Hamiltonian exponential is approximated by an ordered product of two-qubit operator exponentials such that the Trotter step size is enhanced for a small number of terms. Such decomposition directly reflects hardware constraints of distributed quantum computers, where operations on monolithic quantum devices are fast compared to entanglement distribution across separate nodes using interconnects. We simulate nonequilibrium dynamics of transverse-field Ising and XY spin chain models and investigate the impact of locally increased Trotter step sizes that are associated with an increasingly sparse use of the quantum interconnect. We find that the overall quality of the approximation depends smoothly on the local sparsity and that the proliferation of local errors is slow. As a consequence, we show that fast local operations on monolithic devices can be leveraged to obtain an overall improved result fidelity even on distributed quantum computers where the use of interconnects is costly.

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References (51)

  1. F. Arute, K. Arya, R. Babbush, D. Bacon, J. C. Bardin, R. Barends, R. Biswas, S. Boixo, F. G. Brandao, D. A. Buell et al., Quantum supremacy using a programmable superconducting processor, Nature (London) 574, 505 (2019).
  2. H. S. Zhong, H. Wang, Y. H. Deng, M. C. Chen, L. C. Peng, Y. H. Luo, J. Qin, D. Wu, X. Ding, Y. Hu et al., Quantum computational advantage using photons, Science 370, 1460 (2020).
  3. Y. Wu, W. S. Bao, S. Cao, F. Chen, M. C. Chen, X. Chen, T. H. Chung, H. Deng, Y. Du, D. Fan, M. Gong, C. Guo, C. Guo, S. Guo, L. Han, L. Hong, H. L. Huang, Y. H. Huo, L. Li, N. Li et al., Strong Quantum Computational Advantage Using a Superconducting Quantum Processor, Phys. Rev. Lett. 127, 180501 (2021).
  4. L. S. Madsen, F. Laudenbach, M. F. Askarani, F. Rortais, T. Vincent, J. F. F. Bulmer, F. M. Miatto, L. Neuhaus, L. G. Helt, M. J. Collins et al., Quantum computational advantage with a programmable photonic processor, Nature (London) 606, 75 (2022).
  5. R. P. Feynman, Simulating physics with computers, Int. J. Theor. Phys. 21, 467 (1982).
  6. S. Raeisi, N. Wiebe, and B. C. Sanders, Quantum-circuit design for efficient simulations of many-body quantum dynamics, New J. Phys. 14, 103017 (2012).
  7. A. Macridin, P. Spentzouris, J. Amundson, and R. Harnik, Electron-Phonon Systems on a Universal Quantum Computer, Phys. Rev. Lett. 121, 110504 (2018).
  8. A. Smith, M. S. Kim, F. Pollmann, and J. Knolle, Simulating quantum many-body dynamics on a current digital quantum computer, npj Quantum Inf. 5, 106 (2019).
  9. S.-H. Lin, R. Dilip, A. G. Green, A. Smith, and F. Pollmann, Real- and Imaginary-Time Evolution with Compressed Quantum Circuits, PRX Quantum 2, 010342 (2021).
  10. S. P. Jordan, K. S. Lee, and J. Preskill, Quantum algorithms for quantum field theories, Science 336, 1130 (2012).
  11. S. P. Jordan, K. S. Lee, and J. Preskill, Quantum computation of scattering in scalar quantum field theories, Quantum Inf. Comput. 14, 1014 (2014).
  12. E. F. Dumitrescu, A. J. McCaskey, G. Hagen, G. R. Jansen, T. D. Morris, T. Papenbrock, R. C. Pooser, D. J. Dean, and P. Lougovski, Cloud Quantum Computing of an Atomic Nucleus, Phys. Rev. Lett. 120, 210501 (2018).
  13. D. Wecker, B. Bauer, B. K. Clark, M. B. Hastings, and M. Troyer, Gate-count estimates for performing quantum chemistry on small quantum computers, Phys. Rev. A 90, 022305 (2014).
  14. R. Babbush, J. McClean, D. Wecker, A. Aspuru-Guzik, and N. Wiebe, Chemical basis of Trotter-Suzuki errors in quantum chemistry simulation, Phys. Rev. A 91, 022311 (2015).
  15. D. Poulin, M. B. Hastings, D. Wecker, N. Wiebe, A. C. Doherty, and M. Troyer, The trotter step size required for accurate quantum simulation of quantum chemistry, Quantum Inf. Comput. 15, 361 (2015).
  16. Y. Cao, J. Romero, J. P. Olson, M. Degroote, P. D. Johnson, M. Kieferová, I. D. Kivlichan, T. Menke, B. Peropadre, N. P. Sawaya et al., Quantum chemistry in the age of quantum computing, Chem. Rev. 119, 10856 (2019).
  17. Y. Cao, J. Romero, and A. Aspuru-Guzik, Potential of quantum computing for drug discovery, IBM J. Res. Dev. 62, 6 (2018).
  18. J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
  19. A. J. Daley, I. Bloch, C. Kokail, S. Flannigan, N. Pearson, M. Troyer, and P. Zoller, Practical quantum advantage in quantum simulation, Nature (London) 607, 667 (2022).
  20. K. Bharti, A. Cervera-Lierta, T. H. Kyaw, T. Haug, S. Alperin-Lea, A. Anand, M. Degroote, H. Heimonen, J. S. Kottmann, T. Menke et al., Noisy intermediate-scale quantum algorithms, Rev. Mod. Phys. 94, 015004 (2022).
  21. D. Cuomo, M. Caleffi, and A. S. Cacciapuoti, Towards a distributed quantum computing ecosystem, IET Quantum Commun. 1, 3 (2020).
  22. R. Van Meter and S. J. Devitt, The path to scalable distributed quantum computing, Computer 49, 31 (2016).
  23. L. J. Stephenson, D. P. Nadlinger, B. C. Nichol, S. An, P. Drmota, T. G. Ballance, K. Thirumalai, J. F. Goodwin, D. M. Lucas, and C. J. Ballance, High-Rate, High-Fidelity Entanglement of Qubits Across an Elementary Quantum Network, Phys. Rev. Lett. 124, 110501 (2020).
  24. Eagle's Quantum Performance Progress, https://research.ibm.com/blog/eagle-quantum-processor-performance. Accessed: 02-12-2022
  25. IonQ Aria: Practical Performance (Part One), https://ionq.com/posts/july-25-2022-ionq-aria-part-one-practical-performance. Accessed: 02-12-2022.
  26. F. Vatan and C. Williams, Optimal quantum circuits for general two-qubit gates, Phys. Rev. A 69, 032315 (2004).
  27. C. H. Bennett, G. Brassard, S. Popescu, B. Schumacher, J. A. Smolin, and W. K. Wootters, Purification of Noisy Entanglement and Faithful Teleportation via Noisy Channels, Phys. Rev. Lett. 76, 722 (1996).
  28. M. Suzuki, General theory of fractal path integrals with applications to many-body theories and statistical physics, J. Math. Phys. 32, 400 (1991).
  29. H. F. Trotter, On the product of semi-groups of operators, Proc. Amer. Math. Soc. 10, 545 (1959).
  30. M. Suzuki, Generalized Trotter's formula and systematic approximants of exponential operators and inner derivations with applications to many-body problems, Commun. Math. Phys. 51, 183 (1976).
  31. S. Olmschenk, D. N. Matsukevich, P. Maunz, D. Hayes, and C. Monroe, Distant matter qubits, Science 323, 486 (2009).
  32. P. Pfeuty, The one-dimensional Ising model with a transverse field, Ann. Phys. 57, 79 (1970).
  33. P. Calabrese, F. H. Essler, and M. Fagotti, Quantum quench in the transverse field Ising chain: I. Time evolution of order parameter correlators, J. Stat. Mech. (2012) P07016.
  34. In some cases, tighter bounds can be formulated, depending on the structure of the underlying Hamiltonian [42, 45, 46, 51].
  35. See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.5.L022003 for additional data and details on the stochastic sparse Trotterization.
  36. A. Papageorgiou and C. Zhang, On the efficiency of quantum algorithms for Hamiltonian simulation, Quantum. Inf. Proc. 11, 541 (2012).
  37. D. W. Berry, G. Ahokas, R. Cleve, and B. C. Sanders, Efficient quantum algorithms for simulating sparse Hamiltonians, Commun. Math. Phys. 270, 359 (2007).
  38. S. Hadfield and A. Papageorgiou, Divide and conquer approach to quantum Hamiltonian simulation, New J. Phys. 20, 043003 (2018).
  39. A. M. Childs, A. Ostrander, and Y. Su, Faster quantum simulation by randomization, Quantum 3, 182 (2019).
  40. E. Campbell, Random Compiler for Fast Hamiltonian Simulation, Phys. Rev. Lett. 123, 070503 (2019).
  41. Y. Ouyang, D. R. White, and E. T. Campbell, Compilation by stochastic hamiltonian sparsification, Quantum 4, 235 (2020).
  42. M. Heyl, P. Hauke, and P. Zoller, Quantum localization bounds Trotter errors in digital quantum simulation, Sci. Adv. 5, eaau8342 (2019).
  43. E. H. Lieb and D. W. Robinson, The finite group velocity of quantum spin systems, Commun. Math. Phys. 28, 251 (1972).
  44. M. Benedetti, M. Fiorentini, and M. Lubasch, Hardware-efficient variational quantum algorithms for time evolution, Phys. Rev. Res. 3, 033083 (2021).
  45. D. Layden, First-Order Trotter Error from a Second-Order Perspective, Phys. Rev. Lett. 128, 210501 (2022).
  46. L. M. Sieberer, T. Olsacher, A. Elben, M. Heyl, P. Hauke, F. Haake, and P. Zoller, Digital quantum simulation, Trotter errors, and quantum chaos of the kicked top, npj Quantum Inf. 5, 78 (2019).
  47. E. Tham, I. Khait, and A. Brodutch, Quantum circuit optimization for multiple QPUs using local structure, in Proceedings of the 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, Broomfield, CO, 2022), p. 476.
  48. MultiQopt compilation benchmarks, https://entanglednetworks.com/multiqopt. Accessed: 02-12-2022.
  49. M. Motta, C. Sun, A. T. Tan, M. J. O'Rourke, E. Ye, A. J. Minnich, F. G. Brandão, and G. K. L. Chan, Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution, Nat. Phys. 16, 205 (2020).
  50. D. Amaro, C. Modica, M. Rosenkranz, M. Fiorentini, M. Benedetti, and M. Lubasch, Filtering variational quantum algorithms for combinatorial optimization, Quantum Sci. Technol. 7, 015021 (2022).
  51. A. M. Childs, Y. Su, M. C. Tran, N. Wiebe, and S. Zhu, Theory of Trotter Error with Commutator Scaling, Phys. Rev. X 11, 011020 (2021).

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