Ket Programming Guide
Resumo
The development of quantum computing necessitates specialized software tools. Ket is an open-source quantum programming platform that enables gate-based quantum development in Python, supported by a Rust-based runtime library and simulator. This paper presents a programming guide for the Ket platform. It covers the core concepts required to develop quantum applications, including quantum process management, gate operations, measurement strategies, and Hamiltonian construction. Designed for readers with prior knowledge of quantum computing and Python, this guide bridges the gap between theoretical algorithms and practical software implementation. To consolidate these concepts, the paper concludes with a practical tutorial applying QAOA, VQE, and FALQON to the Max-Cut problem, ultimately equipping researchers and developers to fully utilize the Ket platform.Referências
Acharya, R., Abanin, D. A., Aghababaie-Beni, L., et al. (2025). Quantum error correction below the surface code threshold. Nature, 638(8052):920–926.
Amaral, C. A., Oliveira, V. L., Salazar, J. P. L. C., and Duzzioni, E. I. (2026). A Review of Quantum Machine Learning and Quantum-inspired Applied Methods to Computational Fluid Dynamics. Braz J Phys, 56(1):39.
Farhi, E., Goldstone, J., and Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm.
Hadfield, S., Wang, Z., O’Gorman, B., Rieffel, E. G., Venturelli, D., and Biswas, R. (2019). From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz. Algorithms, 12(2):34.
Magann, A. B., Rudinger, K. M., Grace, M. D., and Sarovar, M. (2022). Feedback-Based Quantum Optimization. Phys. Rev. Lett., 129(25):250502.
Mitarai, K., Negoro, M., Kitagawa, M., and Fujii, K. (2018). Quantum circuit learning. Phys. Rev. A, 98(3):032309.
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., and O’Brien, J. L. (2014). A variational eigenvalue solver on a photonic quantum processor. Nat Commun, 5(1):4213.
Rosa, E., Lussi, E., Marchi, J., De Santiago, R., and Duzzioni, E. (2026). Full Quantum Stack: Ket Platform. Braz J Phys, 56(1):45.
Amaral, C. A., Oliveira, V. L., Salazar, J. P. L. C., and Duzzioni, E. I. (2026). A Review of Quantum Machine Learning and Quantum-inspired Applied Methods to Computational Fluid Dynamics. Braz J Phys, 56(1):39.
Farhi, E., Goldstone, J., and Gutmann, S. (2014). A Quantum Approximate Optimization Algorithm.
Hadfield, S., Wang, Z., O’Gorman, B., Rieffel, E. G., Venturelli, D., and Biswas, R. (2019). From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz. Algorithms, 12(2):34.
Magann, A. B., Rudinger, K. M., Grace, M. D., and Sarovar, M. (2022). Feedback-Based Quantum Optimization. Phys. Rev. Lett., 129(25):250502.
Mitarai, K., Negoro, M., Kitagawa, M., and Fujii, K. (2018). Quantum circuit learning. Phys. Rev. A, 98(3):032309.
Peruzzo, A., McClean, J., Shadbolt, P., Yung, M.-H., Zhou, X.-Q., Love, P. J., Aspuru-Guzik, A., and O’Brien, J. L. (2014). A variational eigenvalue solver on a photonic quantum processor. Nat Commun, 5(1):4213.
Rosa, E., Lussi, E., Marchi, J., De Santiago, R., and Duzzioni, E. (2026). Full Quantum Stack: Ket Platform. Braz J Phys, 56(1):45.
Publicado
19/07/2026
Como Citar
ROSA, Evandro C. R. da; MARCHI, Jerusa; SANTIAGO, Rafael de.
Ket Programming Guide. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO E COMUNICAÇÃO QUÂNTICAS (SBCCQ), 1. , 2026, Gramado/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 59-70.
DOI: https://doi.org/10.5753/sbccq.2026.20785.
