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Explore the intersection of quantum computing and machine learning in this comprehensive lecture on Quantum Machine Learning (QML) for optimization. Delve into the era of Noise Intermediate Scale Quantum (NISQ) devices and their impact on quantum computing's future. Discover novel techniques for tackling common barriers in optimization problems, focusing on gradient methods and their challenges such as barren plateaus and local minima. Learn about coordinate transformations and their application in QML algorithms. Examine benchmarks performed on well-known quantum machine learning algorithms to support this innovative approach. Gain insights into the potential advantages of QML over classical paradigms and its promising applications in the near future.
