On Quantum Computers and Artificial Neural Networks
On Quantum Computers and Artificial Neural Networks
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论量子计算机和人工神经网络
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
S. Moraru
中科院分区：
文献类型：
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作者：
F. Neukart;S. Moraru
量子计算机科学与计算神经科学的范式相结合，特别是人工神经网络领域的范式，似乎有希望为人工智能的未来提供一个前景。在这一阐述中，量子人工神经网络不仅提出了在冯诺依曼计算机上模拟的量子力学的影响，而且实际上是在量子计算机上处理的。量子计算机迟早会取代经典的冯诺依曼机器，这一直是这项研究的动机。虽然量子人工神经网络是一个经典的前馈网络，利用量子力学效应，它已经，根据其新奇和差异性，专门一个自己的论文。这样的训练只能在冯·诺依曼机器上模拟，这是非常慢的，实际上不适用（但仍然需要证明定理），尽管后者可以用来模拟适合量子计算的环境。这是在SHOCID（Neukart，2010）项目中实现的，该项目旨在展示和证明量子计算机处理人工神经网络的优势。
Quantum computer science in combination with paradigms from computational neuroscience, specifically those from the field of artificial neural networks, seems to be promising for providing an outlook on a possible future of artificial intelligence. Within this elaboration, a quantum artificial neural network not only apportioning effects from quantum mechanics simulated on a von Neumann computer is proposed, but indeed for being processed on a quantum computer. Sooner or later quantum computers will replace classical von Neumann machines, which has been the motivation for this research. Although the proposed quantum artificial neural network is a classical feed forward one making use of quantum mechanical effects, it has, according to its novelty and otherness, been dedicated an own paper. Training such can only be simulated on von Neumann machines, which is pretty slow and not practically applicable (but nonetheless required for proofing the theorem), although the latter ones may be used to simulate an environment suitable for quantum computation. This is what has been realized during the SHOCID (Neukart, 2010) project for showing and proofing the advantages of quantum computers for processing artificial neural networks.