Synergistic Dynamical Decoupling and Circuit Design for Enhanced Algorithm Performance on Near-Term Quantum Devices (2405.17230v2)
Abstract: Dynamical decoupling (DD) is a promising technique for mitigating errors in near-term quantum devices. However, its effectiveness depends on both hardware characteristics and algorithm implementation details. This paper explores the synergistic effects of dynamical decoupling and optimized circuit design in maximizing the performance and robustness of algorithms on near-term quantum devices. By utilizing eight IBM quantum devices, we analyze how hardware features and algorithm design impact the effectiveness of DD for error mitigation. Our analysis takes into account factors such as circuit fidelity, scheduling duration, and hardware-native gate set. We also examine the influence of algorithmic implementation details, including specific gate decompositions, DD sequences, and optimization levels. The results reveal an inverse relationship between the effectiveness of DD and the inherent performance of the algorithm. Furthermore, we emphasize the importance of gate directionality and circuit symmetry in improving performance. This study offers valuable insights for optimizing DD protocols and circuit designs, highlighting the significance of a holistic approach that leverages both hardware features and algorithm design for the high-quality and reliable execution of near-term quantum algorithms.
- J. Preskill, Quantum computing in the nisq era and beyond, Quantum 2, 79 (2018).
- D. Suter and G. A. Álvarez, Colloquium: Protecting quantum information against environmental noise, Rev. Mod. Phys. 88, 041001 (2016).
- M. A. Ali Ahmed, G. A. Álvarez, and D. Suter, Robustness of dynamical decoupling sequences, Phys. Rev. A 87, 042309 (2013).
- B. Merkel, P. Cova Fariña, and A. Reiserer, Dynamical decoupling of spin ensembles with strong anisotropic interactions, Phys. Rev. Lett. 127, 030501 (2021).
- H. Y. Carr and E. M. Purcell, Effects of diffusion on free precession in nuclear magnetic resonance experiments, Phys. Rev. 94, 630 (1954).
- S. Meiboom and D. Gill, Modified Spin‐Echo Method for Measuring Nuclear Relaxation Times, Review of Scientific Instruments 29, 688 (2004), https://pubs.aip.org/aip/rsi/article-pdf/29/8/688/8343239/688_1_online.pdf .
- A. Maudsley, Modified carr-purcell-meiboom-gill sequence for nmr fourier imaging applications, Journal of Magnetic Resonance (1969) 69, 488 (1986).
- G. A. Álvarez, A. M. Souza, and D. Suter, Iterative rotation scheme for robust dynamical decoupling, Phys. Rev. A 85, 052324 (2012).
- L. Viola, E. Knill, and S. Lloyd, Dynamical decoupling of open quantum systems, Phys. Rev. Lett. 82, 2417 (1999).
- A. M. Souza, G. A. Álvarez, and D. Suter, Effects of time-reversal symmetry in dynamical decoupling, Phys. Rev. A 85, 032306 (2012b).
- A. M. Souza, G. A. Álvarez, and D. Suter, Robust dynamical decoupling for quantum computing and quantum memory, Phys. Rev. Lett. 106, 240501 (2011).
- G. S. Uhrig, Keeping a quantum bit alive by optimized π𝜋\piitalic_π-pulse sequences, Phys. Rev. Lett. 98, 100504 (2007).
- E. Farhi, J. Goldstone, and S. Gutmann, A quantum approximate optimization algorithm, arXiv preprint arXiv:1411.4028 (2014).
- S. Niu and A. Todri-Sanial, Effects of dynamical decoupling and pulse-level optimizations on ibm quantum computers, IEEE Transactions on Quantum Engineering 3, 1 (2022).
- C. Tong, H. Zhang, and B. Pokharel, Empirical learning of dynamical decoupling on quantum processors, arXiv preprint arXiv:2403.02294 (2024).
- Y. Ji, S. Brandhofer, and I. Polian, Calibration-aware transpilation for variational quantum optimization, in 2022 IEEE International Conference on Quantum Computing and Engineering (QCE) (IEEE, 2022) pp. 204–214.
- F. Leymann and J. Barzen, The bitter truth about gate-based quantum algorithms in the nisq era, Quantum Science and Technology 5, 044007 (2020).
- J. J. Vartiainen, M. Möttönen, and M. M. Salomaa, Efficient decomposition of quantum gates, Phys. Rev. Lett. 92, 177902 (2004).
- J. S. Baker and S. K. Radha, Wasserstein solution quality and the quantum approximate optimization algorithm: A portfolio optimization case study (2022), arXiv:2202.06782 .
- Y. Ji, K. F. Koenig, and I. Polian, Optimizing quantum algorithms on bipotent architectures, Phys. Rev. A 108, 022610 (2023a).
- Q. Contributors, Qiskit: An open-source framework for quantum computing, Zenodo: Geneva, Switzerland (2023).
- Y. Ji, K. F. Koenig, and I. Polian, Improving the performance of digitized counterdiabatic quantum optimization via algorithm-oriented qubit mapping, arXiv preprint arXiv:2311.14624 (2023c).
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