A Library for Benchmarking Quantum-Circuit Simulators for Quantum Machine Learning
Resumo
O aprendizado de máquina quântico (QML) é uma das principais aplicações candidatas para o hardware quântico de curto prazo (era NISQ), e muitos modelos de QML – notadamente as quantum support vector machines (QSVMs) – são métodos de kernel cujo kernel é a fidelidade entre os estados quânticos que codificam os dados. Apresentamos o QBenchFrame, uma biblioteca Python de código aberto que compila uma única descrição de circuito em uma representação intermediária agnóstica a backend e a executa, de forma ideal e sob um modelo de despolarização configurável, em Qiskit, Cirq, PennyLane e na biblioteca brasileira Ket, além de emitir o código-fonte em cada linguagem. Sua métrica central é a fidelidade quântica de Uhlmann–Jozsa calculada como um kernel das transpostas conjugadas, F = ⟨ψ| ρ |ψ⟩ – exatamente o kernel quântico de fidelidade que fundamenta as QSVMs.
Referências
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Aoki, N. et al. (2024). Performance analysis of quantum computer simulators across different environments. In 2024 IEEE/ACIS 22nd Int. Conf. on Software Engineering Research, Management and Applications (SERA), pages 115–120.
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Buhrman, H., Cleve, R., Watrous, J., and de Wolf, R. (2001). Quantum fingerprinting. Physical Review Letters, 87:167902.
Cerezo, M., Sone, A., Volkoff, T., Cincio, L., and Coles, P. J. (2021). Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature Communications, 12:1791.
da Rosa, E. C. R. and de Santiago, R. (2022). Ket quantum programming. ACM Journal on Emerging Technologies in Computing Systems, 18(1):1–25.
Griffiths, D. J. (2011). Introduction to Quantum Mechanics. Pearson. Guo, X., Wang, M., and Zhao, J. (2025). QuanBench: benchmarking quantum code generation with large language models. arXiv preprint arXiv:2510.16779.
Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., and Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567:209–212.
Huang, H.-L. (2023). Near-term quantum computing techniques: variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation. Science China Physics, Mechanics & Astronomy.
Huang, H.-Y., Kueng, R., and Preskill, J. (2020). Predicting many properties of a quantum system from very few measurements. Nature Physics, 16:1050–1057.
Jamadagni, A. et al. (2024). Benchmarking quantum computer simulation software packages: state vector simulators. arXiv preprint arXiv:2401.09076.
Knill, E. et al. (2008). Randomized benchmarking of quantum gates. Physical Review A, 77:012307.
Li, B. et al. (2022). Pulse-level noisy quantum circuits with QuTiP. Quantum, 6:630.
Magesan, E., Gambetta, J. M., and Emerson, J. (2011). Scalable and robust randomized benchmarking of quantum processes. Physical Review Letters, 106:180504.
Nguyen, T. et al. (2021). Tensor network quantum virtual machine for simulating quantum circuits at exascale. arXiv preprint arXiv:2104.10523.
Rodríguez-Díaz, F. et al. (2024). An experimental comparison of Qiskit and PennyLane for hybrid quantum-classical support vector machines. In Advances in Artificial Intelligence (CAEPIA 2024), pages 121–130.
Schuld, M. (2021). Supervised quantum machine learning models are kernel methods. arXiv preprint arXiv:2101.11020.
Schuld, M. and Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 122:040504.
Sim, S., Johnson, P. D., and Aspuru-Guzik, A. (2019). Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2:1900070.
Temme, K., Bravyi, S., and Gambetta, J. M. (2017). Error mitigation for short-depth quantum circuits. Physical Review Letters, 119:180509.
Aoki, N. et al. (2024). Performance analysis of quantum computer simulators across different environments. In 2024 IEEE/ACIS 22nd Int. Conf. on Software Engineering Research, Management and Applications (SERA), pages 115–120.
Arute, F. et al. (2019). Quantum supremacy using a programmable superconducting processor. Nature, 574:505–510.
Buhrman, H., Cleve, R., Watrous, J., and de Wolf, R. (2001). Quantum fingerprinting. Physical Review Letters, 87:167902.
Cerezo, M., Sone, A., Volkoff, T., Cincio, L., and Coles, P. J. (2021). Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature Communications, 12:1791.
da Rosa, E. C. R. and de Santiago, R. (2022). Ket quantum programming. ACM Journal on Emerging Technologies in Computing Systems, 18(1):1–25.
Griffiths, D. J. (2011). Introduction to Quantum Mechanics. Pearson. Guo, X., Wang, M., and Zhao, J. (2025). QuanBench: benchmarking quantum code generation with large language models. arXiv preprint arXiv:2510.16779.
Havlíček, V., Córcoles, A. D., Temme, K., Harrow, A. W., Kandala, A., Chow, J. M., and Gambetta, J. M. (2019). Supervised learning with quantum-enhanced feature spaces. Nature, 567:209–212.
Huang, H.-L. (2023). Near-term quantum computing techniques: variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation. Science China Physics, Mechanics & Astronomy.
Huang, H.-Y., Kueng, R., and Preskill, J. (2020). Predicting many properties of a quantum system from very few measurements. Nature Physics, 16:1050–1057.
Jamadagni, A. et al. (2024). Benchmarking quantum computer simulation software packages: state vector simulators. arXiv preprint arXiv:2401.09076.
Knill, E. et al. (2008). Randomized benchmarking of quantum gates. Physical Review A, 77:012307.
Li, B. et al. (2022). Pulse-level noisy quantum circuits with QuTiP. Quantum, 6:630.
Magesan, E., Gambetta, J. M., and Emerson, J. (2011). Scalable and robust randomized benchmarking of quantum processes. Physical Review Letters, 106:180504.
Nguyen, T. et al. (2021). Tensor network quantum virtual machine for simulating quantum circuits at exascale. arXiv preprint arXiv:2104.10523.
Rodríguez-Díaz, F. et al. (2024). An experimental comparison of Qiskit and PennyLane for hybrid quantum-classical support vector machines. In Advances in Artificial Intelligence (CAEPIA 2024), pages 121–130.
Schuld, M. (2021). Supervised quantum machine learning models are kernel methods. arXiv preprint arXiv:2101.11020.
Schuld, M. and Killoran, N. (2019). Quantum machine learning in feature Hilbert spaces. Physical Review Letters, 122:040504.
Sim, S., Johnson, P. D., and Aspuru-Guzik, A. (2019). Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Advanced Quantum Technologies, 2:1900070.
Temme, K., Bravyi, S., and Gambetta, J. M. (2017). Error mitigation for short-depth quantum circuits. Physical Review Letters, 119:180509.
Publicado
19/10/2026
Como Citar
BESSA, João Alfredo; SANTOS, Carlos Eduardo; FREITAS, Rosiane de.
A Library for Benchmarking Quantum-Circuit Simulators for Quantum Machine Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 1687-1698.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.31291.
