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- W4210366277 abstract "Why should we look at quantum computing in machine learning? Apart from a speedup and increased storage capacity, quantum computing has further benefits for machine learning algorithms. Learning models lie at the core of data mining, a complex process of extracting meaningful information from large volumes of data. We expect a machine learning model to generalize well beyond a limited training collection. On classical computers, we are constrained by convexity conditions or heuristics to keep computational time under control. Quantum computers do not suffer from these issues in optimization problems; hence, we can achieve better generalization performance. Classical computers excel at other tasks; hence, a heterogeneous model is likely to prevail. Dozens of approaches have already been published on quantum machine learning—we briefly overview the major ones. We further mention classical algorithms that borrow metaphors from quantum mechanics; this is also a prolific area of research." @default.
- W4210366277 created "2022-02-08" @default.
- W4210366277 date "1971-12-01" @default.
- W4210366277 modified "2023-09-26" @default.
- W4210366277 title "Research news" @default.
- W4210366277 doi "https://doi.org/10.1016/0011-2275(71)90282-7" @default.
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