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SupermarQ: A Scalable Quantum Benchmark Suite

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arxiv 2202.11045 v3 pith:XWPUK35E submitted 2022-02-22 quant-ph cs.AR

SupermarQ: A Scalable Quantum Benchmark Suite

Teague Tomesh , Pranav Gokhale , Victory Omole , Gokul Subramanian Ravi , Kaitlin N. Smith , Joshua Viszlai , Xin-Chuan Wu , Nikos Hardavellas
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Margaret R. Martonosi Frederic T. Chong
This is my paper
classification quant-ph cs.AR
keywords quantumbenchmarksupermarqsuitebenchmarkingmeasureperformancescalable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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The emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. This problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

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    Cost-aware ranking of cloud QPUs via QFC disagrees with fidelity-only ranking; billing model, not hardware, fixes how the score scales with shot count.

  5. A Physics-Informed Neuro-Fuzzy Framework for Quantum Error Attribution

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