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Quantum Machine Learning- Using Quantum Computation in Artificial Intelligence and Deep Neural Networks--Quantum Computation and Machine Learning in Artificial Intelligence

机译：量子机械学习 - 在人工智能中使用量子计算和深度神经网络 - 量子计算和人工智能机器学习

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摘要

Machine Learning or Artificial Intelligence basically involves tasks of modifying and supervising problems taken as vectors in multi-dimensional space. The Primitive algorithms which are used take Polynomial Time for computing such vector problems which are not fruitful for us, on the other hand, Quantum algorithms have the capability to solve such vector problems in a considerable amount of time by using Quantum-Mechanical operations. For example, we can perform a Database Search in a time which is Quadratic-ally faster than the primitive search algorithm. Quantum Algorithms rely on Quantum physics and therefore the algorithms are Incoherent in nature and this property makes them more interesting to study. In this paper, we provide the insights of Quantum Machine Learning and we formally prove that the Execution Time of the algorithm is greatly optimized with the help of Adiabatic Quantum Learning. Also, we prove that Quantum Associative Memories can store exponentially more data than its primitive counterparts. Data mining concept is very similar to Machine Learning and we will also show how QML will be beneficial in such cause as well.
机译：机器学习或人工智能基本上涉及修改和监督作为多维空间中的向量的问题的任务。用于计算对我们不富有成效的多项式时间，另一方面，量子算法具有通过使用量子机械操作在相当大的时间内解决这些载体问题的能力。例如，我们可以在比原始搜索算法快速的时间内执行数据库搜索。量子算法依赖量子物理学依赖于量子物理，因此算法本质上不连贯，这家属性使他们更有趣。在本文中，我们提供量子机器学习的见解，我们正式证明算法的执行时间是在绝热量子学习的帮助下大大优化。此外，我们证明了量子关联存储器可以比其原始对应物更多数据。数据挖掘概念与机器学习非常相似，我们也会显示QML在这种情况下如何有益。

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