Quantum computing’s theoretical potential to exponentially speed up deep learning stands in sharp contrast to the current reality. Implementations are imperfect, suffering from noise and poor coherence times, and scalability limitations. In this talk, we explore how quantum-enhanced machine learning plays a complementary role to classical techniques, rather than acting as a replacement. We discuss relevant computing paradigms, such as quantum annealing and gate-model quantum computing over discrete or continuous variables that are performed efficiently with hybrid classical-quantum protocols.
Publisher: Amazon Web Services
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