论文标题
量子人工智能的圣杯：加速机器学习管道的主要挑战
The Holy Grail of Quantum Artificial Intelligence: Major Challenges in Accelerating the Machine Learning Pipeline
论文作者
论文摘要
我们讨论量子计算与人工智能之间的协同连接。在调查了当前人工智能的当前方法并将它们与机器学习过程的形式模型联系起来之后，我们推导了量子人工智能的未来的四个主要挑战：（i）用更快的量子算法替换迭代培训，（ii）将大量数据的构建工具和整体构建工具融合到（III）中，（iii），（iii），（iii），（iii），（iii）（iii）组合（iii）组合（iii），组合组合的组合工具（III）观察到的益处确实源于算法的量子特性。
We discuss the synergetic connection between quantum computing and artificial intelligence. After surveying current approaches to quantum artificial intelligence and relating them to a formal model for machine learning processes, we deduce four major challenges for the future of quantum artificial intelligence: (i) Replace iterative training with faster quantum algorithms, (ii) distill the experience of larger amounts of data into the training process, (iii) allow quantum and classical components to be easily combined and exchanged, and (iv) build tools to thoroughly analyze whether observed benefits really stem from quantum properties of the algorithm.
