# SupermarQ: A Scalable Quantum Benchmark Suite

arXiv: https://arxiv.org/abs/2202.11045 · HTML: https://mlsanity.com/paper/2202.11045/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2022.

## Built on by (most cited first, 14 of 14)

- [Quantum Architecture Search: A Survey](https://mlsanity.com/paper/2406.06210/) (2024)
- [Compiler Optimization for Quantum Computing Using Reinforcement Learning](https://mlsanity.com/paper/2212.04508/) (2022)
- [NAPA: Intermediate-level Variational Native-pulse Ansatz for Variational Quantum Algorithms](https://mlsanity.com/paper/2208.01215/) (2022)
- [Learning to rank quantum circuits for hardware-optimized performance enhancement](https://mlsanity.com/paper/2404.06535/) (2024)
- [EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data](https://mlsanity.com/paper/2503.14473/) (2025)
- [Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK](https://mlsanity.com/paper/2308.04082/) (2023)
- [Training Quantum Boltzmann Machines with Coresets](https://mlsanity.com/paper/2307.14459/) (2023)
- [Profiling quantum circuits for their efficient execution on single- and multi-core architectures](https://mlsanity.com/paper/2407.12640/) (2024)
- [Qompose: A Technique to Select Optimal Algorithm- Specific Layout for Neutral Atom Quantum Architectures](https://mlsanity.com/paper/2409.19820/) (2024)
- [Featuremetric benchmarking: Quantum computer benchmarks based on circuit features](https://mlsanity.com/paper/2504.12575/) (2025)
- [Platform-Agnostic Modular Architecture for Quantum Benchmarking](https://mlsanity.com/paper/2510.08469/) (2025)
- [InferQ: A Database-Oriented Benchmark for Quantum Circuits Simulation](https://mlsanity.com/paper/2607.29134/) (2026)
- [Transpiler Autotuning with Predictive Models for Quantum Circuit Optimization](https://mlsanity.com/paper/2607.29145/) (2026)
- [MQSS-Selector: RL-Guided Pass Selection for an MLIR Compilation Pipeline](https://mlsanity.com/paper/2609.30104/) (2026)

## 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.