Navigating the Dynamic Noise Landscape of Variational Quantum Algorithms with QISMET

Ravi, Gokul Subramanian; Smith, Kaitlin N.; Baker, Jonathan M.; Kannan, Tejas; Earnest, Nathan; Javadi-Abhari, Ali; Hoffmann, Henry; Chong, Frederic T.

doi:10.1145/3575693.3575739

Navigating the Dynamic Noise Landscape of Variational Quantum Algorithms with QISMET

Conference · 30 January 2023 · Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2

DOI:https://doi.org/10.1145/3575693.3575739· OSTI ID:1986250

Ravi, Gokul Subramanian ^[1]; Smith, Kaitlin N. ^[1]; Baker, Jonathan M. ^[1]; Kannan, Tejas ^[1]; Earnest, Nathan ^[2]; Javadi-Abhari, Ali ^[2]; Hoffmann, Henry ^[1]; Chong, Frederic T. ^[1]

University of Chicago, IL (United States)
International Business Machines, Armonk, NY (United States)

In the Noisy Intermediate Scale Quantum (NISQ) era, the dynamic nature of quantum systems causes noise sources to constantly vary over time. Transient errors from the dynamic NISQ noise landscape are challenging to comprehend and are especially detrimental to classes of applications that are iterative and/or long-running, and therefore their timely mitigation is important for quantum advantage in real-world applications. The most popular examples of iterative long-running quantum applications are variational quantum algorithms (VQAs). Iteratively, VQA’s classical optimizer evaluates circuit candidates on an objective function and picks the best circuits towards achieving the application’s target. Noise fluctuation can cause a significant transient impact on the objective function estimation of the VQA iterations’ tuning candidates. This can severely affect VQA tuning and, by extension, its accuracy and convergence. Furthermore, this paper proposes QISMET: Quantum Iteration Skipping to Mitigate Error Transients, to navigate the dynamic noise landscape of VQAs. QISMET actively avoids instances of high fluctuating noise which are predicted to have a significant transient error impact on specific VQA iterations. To achieve this, QISMET estimates transient error in VQA iterations and designs a controller to keep the VQA tuning faithful to the transient-free scenario. By doing so, QISMET efficiently mitigates a large portion of the transient noise impact on VQAs and is able to improve the fidelity by 1.3x-3x over a traditional VQA baseline, with 1.6-2.4x improvement over alternative approaches, across different applications and machines.

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Research Organization:: University of Chicago, IL (United States)

Sponsoring Organization:: USDOE; National Science Foundation (NSF)

DOE Contract Number:: SC0020289

OSTI ID:: 1986250

Journal Information:: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, Conference: ASPLOS '23: 28. ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Vancouver, BC (Canada), 25-29 Mar 2023

Country of Publication:: United States

Language:: English

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