🏆 Our TÜBİTAK ARDEB 1001 project proposal on Quantum Computing has been awarded!
Title: Design of Control Based Quantum Algorithms Using Measurement Based Quantum Computation
Summary: In alignment with the 2025 International Year of Quantum Science and Technology, this
project aims to bridge the gap between quantum theory and real-world optimization
challenges by tackling circuit depth and error tolerance issues. The research focuses
on re-engineering the Feedback-Based Quantum Optimization (FALQON) algorithm,
which eliminates the complexities of classical parameter optimization, within
the framework of Measurement-Based Quantum Computation (MBQC). Utilizing
Lyapunov control theory, the project seeks to develop novel algorithms that reduce
circuit depth and sampling complexity for both constrained and unconstrained
binary optimization problems. These algorithms will be implemented via Qiskit and
validated on IBM’s quantum hardware. Ultimately, this study aims to position Türkiye
at the forefront of the second quantum revolution by fostering high-level expertise
and providing scalable solutions for the Noisy Intermediate-Scale Quantum (NISQ)
era.
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Advances in quantum information science, known as the second quantum revolution, have led UNESCO to designate 2025 as the International Year of Quantum Science and Technology. Quantum computing is among the most supported second-generation quantum technologies during this transformative period. A crucial advancement enabling quantum computing's integration into daily life is quantum optimization algorithms. This project proposal aims to position Turkey actively within these developments by enhancing human resources and expertise in quantum computing and optimization. The project introduces a novel approach, aiming to improve circuit depth and sampling complexity of feedback-based quantum algorithms using measurement-based quantum computing (MBQC).
Circuit-based quantum computing can solve some problems more efficiently than classical methods (Nielsen ve Chuang, 2020); however, current quantum hardware limitations, notably insufficient qubit numbers for error correction, demand alternative strategies. Variational and hybrid algorithms (Farhi ve diğerleri, 2014; Peruzzo ve diğerleri, 2014) combining classical and quantum resources have emerged as prominent solutions, although optimizing quantum circuit parameters classically presents significant challenges (Blekos ve diğerleri, 2024). To address this, Magan et al. (2022) proposed feedback-based quantum optimization (FALQON), utilizing quantum control theory to set parameters directly via quantum circuits. Originally developed for unconstrained binary optimization problems, FALQON has been extended to other optimization scenarios in studies involving the project leader. This project's novelty is adapting these circuit-based quantum algorithms into the MBQC framework, offering significant advantages for current noisy intermediate-scale quantum (NISQ) devices.
Measurement-based implementations of widely studied variational quantum algorithms such as QAOA (Farhi ve diğerleri, 2014) and VQE (Peruzzo ve diğerleri, 2014) have demonstrated improvements in circuit depth and gate complexity (Kaldenbach ve Heller, 2024). However, measurement-based versions of feedback-based algorithms, like FALQON, have not yet been explored. This project aims to develop MBQC implementations of FALQON, focusing on achieving the lower sampling complexity and circuit depth crucial in the NISQ era. Initially, a measurement-based FALQON algorithm will be developed for unconstrained binary optimization, tested on the MaxCut problem. The second objective will extend the approach to constrained optimization problems. Thirdly, the project will generalize the method for identifying excited eigenvectors. Lastly, the project aims to develop a measurement-based circuit for determining the first K eigenvectors.
Methods include Lyapunov control and measurement-based circuit design, previously applied successfully in related literature. The success criteria are detailed in the project's work packages. To verify these criteria, developed algorithms will be implemented using Qiskit and executed on IBM’s quantum computers. For problems too extensive for real hardware, quantum simulators will be used.
The project seeks to foster synergy among Turkish public and private sector entities such as TÜBİTAK Ulakbim and QndCo, as well as academic researchers. Outcomes will attract researchers and students, significantly contributing to developing skilled personnel essential for quantum computing advancement.
🎤 Özkan Karabacak Talked About Quantum Optimization on November 23, 2024
Speaker: Özkan Karabacak
Topic: Lyapunov Control Theory in Quantum Optimization
Duration: 30 minutes
Özkan Karabacak delivered a 30-minute talk explaining how
Lyapunov's control theory can be applied to quantum optimization.
The talk provided a general overview of how control strategies can improve quantum algorithms.
View on YouTube
