How to Rethink Quantum Machine Learning - IPAM at UCLA

Explore the cutting-edge intersection of quantum computing and machine learning in this thought-provoking conference talk by Maria Schuld from Xanadu Quantum Technologies. Delve into a critical examination of current approaches in quantum machine learning, particularly focusing on parametrized quantum circuits trained by gradient descent methods. Discover preliminary results from two ongoing studies that challenge conventional wisdom: one casting doubt on the celebrated "quantum over classical" performance through systematic comparisons, and another investigating core quantum computing routines like Shor's algorithm from a generalization perspective. Gain insights into potentially revolutionary ways of conceptualizing the fusion of quantum computing and machine learning, and consider how these findings might reshape the future of the field.