Final Remarks

The Ket platform offers a robust, Pythonic interface for quantum software development, leveraging a highly optimized Rust backend for efficient context management and circuit compilation. The project is developed within the Quantum Computing Group at the Federal University of Santa Catarina (GCQ-UFSC). In 2025, Ket was recognized by the Brazilian Computer Society (SBC) with the Innovation Seal (Selo de Inovação) for its significant contribution to the national development of quantum computing.

Ket remains a project in constant development. While it is currently well-equipped for state-of-the-art quantum computing research, the future roadmap for the platform includes the development of domain-specific libraries and integration with pulse-level control for quantum error suppression and mitigation. Furthermore, to ensure the platform is fully prepared for the upcoming paradigm of fault-tolerant quantum computing, the roadmap specifically targets the implementation of quantum error correction.

References

[1]

Rajeev Acharya, Dmitry A. Abanin, Laleh Aghababaie-Beni, and others. Quantum error correction below the surface code threshold. Nature, 638(8052):920–926, February 2025. doi:10.1038/s41586-024-08449-y.

[2]

Evandro Rosa, Eduardo Lussi, Jerusa Marchi, Rafael De Santiago, and Eduardo Duzzioni. Full Quantum Stack: Ket Platform. Braz J Phys, 56(1):45, February 2026. doi:10.1007/s13538-025-01981-w.

[3]

Stuart Hadfield, Zhihui Wang, Bryan O'Gorman, Eleanor G. Rieffel, Davide Venturelli, and Rupak Biswas. From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz. Algorithms, 12(2):34, February 2019. doi:10.3390/a12020034.

[4]

Simon J Devitt, William J Munro, and Kae Nemoto. Quantum error correction for beginners. Rep. Prog. Phys., 76(7):076001, July 2013. doi:10.1088/0034-4885/76/7/076001.

[5]

Cesar A. Amaral, Vinícius L. Oliveira, Juan P. L. C. Salazar, and Eduardo I. Duzzioni. A Review of Quantum Machine Learning and Quantum-inspired Applied Methods to Computational Fluid Dynamics. Braz J Phys, 56(1):39, February 2026. doi:10.1007/s13538-025-01959-8.

[6]

K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii. Quantum circuit learning. Phys. Rev. A, 98(3):032309, September 2018. doi:10.1103/PhysRevA.98.032309.

[7]

Alicia B. Magann, Kenneth M. Rudinger, Matthew D. Grace, and Mohan Sarovar. Feedback-Based Quantum Optimization. Phys. Rev. Lett., 129(25):250502, December 2022. doi:10.1103/PhysRevLett.129.250502.

[8]

Edward Farhi, Jeffrey Goldstone, and Sam Gutmann. A Quantum Approximate Optimization Algorithm. 2014. doi:10.48550/ARXIV.1411.4028.

[9]

Alberto Peruzzo, Jarrod McClean, Peter Shadbolt, Man-Hong Yung, Xiao-Qi Zhou, Peter J. Love, Alán Aspuru-Guzik, and Jeremy L. O'Brien. A variational eigenvalue solver on a photonic quantum processor. Nat Commun, 5(1):4213, July 2014. doi:10.1038/ncomms5213.