SupermarQ: A Scalable Quantum Benchmark Suite
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Abstract (arXiv)
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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2022.
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Citing papers per year: 2022: 2; 2023: 2; 2024: 4; 2025: 3; 2026: 3.
14 papers.
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- 2024Quantum Architecture Search: A Surveycited by 31
- 2022Compiler Optimization for Quantum Computing Using Reinforcement Learningcited by 11
- 2022NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithmscited by 4
- 2024Learning to rank quantum circuits for hardware-optimized performance enhancementcited by 4
- 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Datacited by 2
- 2023Application-Oriented Benchmarking of Quantum Generative Learning Using QUARKcited by 1
- 2023Training Quantum Boltzmann Machines with Coresetscited by 0
- 2024Profiling quantum circuits for their efficient execution on single- and multi-core architecturescited by 0
- 2024Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architecturescited by 0
- 2025Featuremetric benchmarking: Quantum computer benchmarks based on circuit featurescited by 0
- 2025Platform-Agnostic Modular Architecture for Quantum Benchmarkingcited by 0
- 2026InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulationcited by 0
- 2026MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline24 Sep 2026
- 2026Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization31 Jul 2026
- 2026InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation31 Jul 2026
- 2025Platform-Agnostic Modular Architecture for Quantum Benchmarking9 Oct 2025
- 2025Featuremetric benchmarking: Quantum computer benchmarks based on circuit features17 Apr 2025
- 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data18 Mar 2025
- 2024Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architectures29 Sep 2024
- 2024Profiling quantum circuits for their efficient execution on single- and multi-core architectures17 Jul 2024
- 2024Quantum Architecture Search: A Survey10 Jun 2024
- 2024Learning to rank quantum circuits for hardware-optimized performance enhancement9 Apr 2024
- 2023Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK8 Aug 2023
- 2023Training Quantum Boltzmann Machines with Coresets26 Jul 2023
- 2024Quantum Architecture Search: A Surveycited by 31
- 2022Compiler Optimization for Quantum Computing Using Reinforcement Learningcited by 11
- 2022NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithmscited by 4
- 2024Learning to rank quantum circuits for hardware-optimized performance enhancementcited by 4
- 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Datacited by 2
- 2023Application-Oriented Benchmarking of Quantum Generative Learning Using QUARKcited by 1
- 2023Training Quantum Boltzmann Machines with Coresetscited by 0
- 2024Profiling quantum circuits for their efficient execution on single- and multi-core architecturescited by 0
- 2024Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architecturescited by 0
- 2025Featuremetric benchmarking: Quantum computer benchmarks based on circuit featurescited by 0
- 2025Platform-Agnostic Modular Architecture for Quantum Benchmarkingcited by 0
- 2026InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulationcited by 0
12 most cited of 14 All 14 (JSON)
- 2026MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline24 Sep 2026
- 2026Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization31 Jul 2026
- 2026InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation31 Jul 2026
- 2025Platform-Agnostic Modular Architecture for Quantum Benchmarking9 Oct 2025
- 2025Featuremetric benchmarking: Quantum computer benchmarks based on circuit features17 Apr 2025
- 2025EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data18 Mar 2025
- 2024Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architectures29 Sep 2024
- 2024Profiling quantum circuits for their efficient execution on single- and multi-core architectures17 Jul 2024
- 2024Quantum Architecture Search: A Survey10 Jun 2024
- 2024Learning to rank quantum circuits for hardware-optimized performance enhancement9 Apr 2024
- 2023Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK8 Aug 2023
- 2023Training Quantum Boltzmann Machines with Coresets26 Jul 2023
12 newest of 14 All 14 (JSON)