The webinar, Iterative warm-started QAOA for QUBO problems on noisy quantum hardware, will be led by Marek Lampart, Ryszard Kukulski, and Adam Bílek from IT4Innovations and is organised as part of the international Q-Neko project.
They will focus on using the QAOA (Quantum Approximate Optimisation Algorithm) to solve optimisation problems on today’s noisy quantum hardware, demonstrate a practical approach, and use examples to show how advanced methods and reinforcement learning can improve the results of quantum computations.
The webinar will take place online on 27 October and is intended for participants with a basic understanding of gate-based quantum computing. Knowledge of Qiskit is an advantage. No background in reinforcement learning is needed.