Demonstrating NISQ era challenges in algorithm design on IBM’s 20 qubit quantum computer
Source abstract
Abstract
As superconducting qubits continue to advance technologically, the realization of quantum algorithms from theoretical abstraction to physical implementation requires knowledge of both quantum circuit construction as well as hardware limitations. In this study, we present results from experiments run on IBM’s 20-qubit “Poughkeepsie” architecture, with the goal of demonstrating various qubit qualities and challenges that arise in designing quantum algorithms. These include experimentally measuring T1 and T2 coherence times, gate fidelities, sequential CNOT gates, techniques for handling ancilla qubits, and finally CCNOT and QFT† circuits implemented on several different qubit geometries. Our results demonstrate various techniques for improving quantum circuits, which must compensate for limited connectivity, either through the use of SWAP gates or additional ancilla qubits.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Quantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Quantum-Dot Cellular Automata
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Air Force Research Lab, Information Directorate 1 , Rome, New York 13441, USA
Air Force Academy 2 , Colorado Springs, Colorado 80840, USA
Funding
National Research Council
References (36)
Show source-provided references
1. Quantum · 2018 · 2 · 7910.22331/q-2018-08-06-79 ↗
2. SIAM J. Sci. Stat. Comput. · 1997 · 26 · 148410.1137/s0097539795293172 ↗
3. Rev. Mod. Phys. · 2010 · 82 · 115510.1103/revmodphys.82.1155 ↗
4. Phys. Rev. Lett. · 2018 · 121 · 09050210.1103/PhysRevLett.121.090502 ↗
5. Appl. Phys. Rev. · 2019 · 6 · 02131810.1063/1.5089550 ↗
6. M. Kjaergaard, M. E. Schwartz, J. Braumüller, P. Krantz, J. I.-J. Wang, S. Gustavsson, and W. D. Oliver, arXiv:1905.13641 (2019).
7. Phys. Rev. Lett. · 2017 · 119 · 18050910.1103/physrevlett.119.180509 ↗
8. Phys. Rev. Lett. · 2019 · 122 · 08050410.1103/physrevlett.122.080504 ↗
9. P. Murali, D. C. McKay, M. Martonosi, and A. Javadi-Abhari, arXiv:2001.02826 (2020).
10. Phys. Rev. A · 1995 · 52 · R249310.1103/physreva.52.r2493 ↗
11. Phys. Rev. A · 1996 · 54 · 109810.1103/physreva.54.1098 ↗
12. Phys. Rev. Lett. · 1996 · 77 · 79310.1103/physrevlett.77.793 ↗
13. 2019
14. 2019
15. 2019
16. D. Koch, L. Wessing, and P. M. Alsing, arXiv:1903.04359 (2019).
17. D. Koch, S. Patel, L. Wessing, and P. M. Alsing, arXiv:2008.10647 (2020).
18. Phys. Rev. · 1950 · 80 · 58010.1103/physrev.80.580 ↗
19. Phys. Rev. · 1950 · 78 · 69510.1103/physrev.78.695 ↗
20. Phys. Rev. B · 2005 · 72 · 121303(R)10.1103/physrevb.72.121303 ↗
21. D. Coppersmith, arXiv:quant-ph/0201067v1 (1994).
22. de Bakker · 1980; 1988 · 63210.1007/3-540-10003-2 ↗
23. Proc. Natl. Acad. Sci. U. S. A. · 2017 · 114 · 1310.1073/pnas.1618020114 ↗
24. S. S. Tannu and M. K. Qureshi, arXiv:quant-ph/1805.10224 (2018).
25. P. J. Coles, S. Eidenbenz et al., arXiv:1804.03719 (2018).
26. 2018
27. A. May, L. Schlieper, and J. Schwinger, arXiv:1910.00802 (2019).
28. H. Mohammadbagherpoor, Y.-H. Oh, P. Dreher, A. Singh, X. Yu, and A. J. Rindos, arXiv:1910.11696 (2019).
29. E. Buksman, A. L. Fonseca de Oliveira, and C. Allende, arXiv:1912.07486 (2019).
30. P. Q. Cruz, G. Catarina, R. Gautier, and J. Fernández-Rossier, arXiv:1910.06265 (2019).
31. 1997
32. Phys. Rev. A · 1995 · 52 · 345710.1103/physreva.52.3457 ↗
33. Quantum Inf. Comput. · 2017 · 17 · 673
34. A. Y. Kitaev, arXiv:quant-ph/9511026 (1995).
35. E. Farhi, J. Goldstone, and S. Gutmann, arXiv:1411.4028 (2014).
36. 2000