Quantum Datasets

Benchmarking datasets

This section gathers datasets specifically used for the benchmarking of quantum computers. These datasets are classified using an extension of the quantum compilation flow introduced by N. Quetschlich et al. in [1]. The compilation flow consists of several abstraction levels, ranging from high-level application descriptions to hardware-specific implementations:

Application level: The instance is described as a problem without the quantum circuit implementation.
Algorithmic level: The problem is formulated as a quantum circuit composed of high-level blocks that do not consider the quantum computer’s topology or gate set (hardware-agnostic). For example, a block could be a variational quantum algorithm layer or a quantum Fourier transform.
Target-independent level: The circuit defined at the algorithmic level is synthesized in high-level building blocks. It is done by specifying the parameters of parametrized gates and decomposing high-level blocks (for example, decomposing a QFT block into two-qubit and single-qubit quantum gates).
Target-dependent native gate set level: The circuit is synthesized into a gate set compatible with the quantum computer.
Target-dependent mapped level: The quantum circuit is synthesized to consider the layout of the qubit connectivity. This step might involve additional operations such as SWAP gates to comply with the connectivity constraints of the quantum hardware. The datasets are presented by creation date.
Pulse-level: The quantum circuit is further synthesized to a physical pulse sequence that represents the physical interactions with the qubits.

The following table relates each dataset to the abstraction level it benchmarks.

Date & Ref	Name	Affiliation	Type	Targetted Application	QC model	Abstraction levels	Implementation	Recent activity	Links
2025 [2]	QOBLIB	Universities IBM< br/>Kipu Quantum Quantagonia	problem instances	Optimization	gate and analog	1	depend on the problem class	x	dataset
2025 [3]	QMProt	Lighthouse-dig Barcelona	Hamiltonian operators	Chemistry	gate and analog	2	PennyLane Hamiltonian	x	dataset
2025 [4]	PennyLane MoleculesPennyLane Molecules dataset	PennyLane	Hamiltonian operators	Chemistry	gate and analog	2	PennyLane Hamiltonian	x	dataset
2024 [5]	PennyLane Spin systemsPennyLane Spin systems dataset	PennyLane	Hamiltonian operators	Condensed matter models simulation	gate and analog	2	PennyLane Hamiltonian	x	dataset
2024 [6]	PennyLane Op-T-mizeOp-T-mize	PennyLane	quantum sub routines	Circuit compilation	gate	4	PennyLane Quantum script	x	dataset
2023 [7]	HamLibHamLib	Intel Lab Sandia Lab Oxford university	Hamiltonian operators	Optimization,Condensed matter models simulation,Chemistry	gate and analog	2	OpenFermion	x	dataset loading functions
2022 [1]	MQTBenchMQTBench	TU Munich	quantum algorithms	Basic quantum routines,Optimization,Random circuit sampling	gate	2, 3, 4, 5	OpenQasm 3.0	x	dataset
2022 [8]	VeriQBenchVeriQBench	Chinese academy of science Tsinghua University	quantum algorithms and sub routines	Basic quantum routines,Random circuit sampling,Error correction routines	gate	2	OpenQasm 2.0	x	<a href="a href="https://github.com/Veri-Q/Benchmark" target="_blank">dataset</a>" target="_blank">github.com</a>
2008 [9]	RevLibRevLib	University of Bremen	quantum sub routines	quantum subroutines	gate	2	Custom formalism		dataset

Quantum Machine Learning (QML) datasets

Other datasets references

The reader may refer to other maintained datasets references:

Pennylane datasets

References

[1]N. Quetschlich, L. Burgholzer, and R. Wille, “MQT Bench: Benchmarking software and design automation tools for quantum computing,” Quantum, vol. 7, p. 1062, 2023.
[2]T. Koch et al., “Quantum Optimization Benchmark Library–The Intractable Decathlon,” arXiv preprint arXiv:2504.03832, 2025.
[3]L. C. Sala and P. Atchade-Adelomou, “A Comprenhensive Dataset of Quantum Properties for Proteins,” 2025, doi: 10.48550/ARXIV.2505.08956. [Online]. Available at: https://arxiv.org/abs/2505.08956
[4]U. Azad and S. Fomichev, “PennyLane Molecules.” 2025 [Online]. Available at: https://pennylane.ai/datasets/collection/qchem
[5]U. Azad and S. Fomichev, “PennyLane Molecules.” 2025 [Online]. Available at: https://pennylane.ai/datasets/collection/qchem
[6]K. Kottmann, “T-gate optimization.” 2024 [Online]. Available at: https://pennylane.ai/datasets/op-T-mize
[7]N. P. D. Sawaya et al., “HamLib: A library of Hamiltonians for benchmarking quantum algorithms and hardware,” Quantum, vol. 8, p. 1559, 2024.
[8]K. Chen et al., “VeriQBench: A benchmark for multiple types of quantum circuits,” arXiv preprint arXiv:2206.10880, 2022.
[9]R. Wille, D. Gro, L. Teuber, G. W. Dueck, and R. Drechsler, “RevLib: An Online Resource for Reversible Functions and Reversible Circuits,” in 38th International Symposium on Multiple Valued Logic (ismvl 2008), 2008, doi: 10.1109/ismvl.2008.43 [Online]. Available at: http://dx.doi.org/10.1109/ISMVL.2008.43
[10]J. R. McClean et al., “OpenFermion: the electronic structure package for quantum computers,” Quantum Science and Technology, vol. 5, no. 3, p. 034014, 2020.