This research project aims to enhance Quantum Error Correction (QEC) decoders by integrating advanced graph-based algorithms on FPGA, addressing challenges like scalability and real-time adaptability. It focuses on using dynamic graph algorithms, matching and flow techniques, and reinforcement learning to improve error correction in stabilizer codes. The project will develop an FPGA-based prototype, demonstrating efficiency and adaptability in quantum computing scenarios. Expected outcomes include a novel, scalable QEC decoder and FPGA implementation showcasing improved performance and contributions to QEC methodologies, fostering progress toward reliable quantum computing.
https://digilent.com/shop/zedboard-zynq-7000-arm-fpga-soc-development-board/
https://indico.cern.ch/event/1433194/timetable/?view=standard_numbered
https://qarlab.de/en/category/qar-lab-en/
https://aqora.io/events/cern-hep-challenge-2025
https://www.linkedin.com/pulse/13-companies-using-quantum-theory-accelerate-drug-andrii-buvailo-/