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Quantum-mechanical tunneling in associative neutral network

机译:关联神经网络中的量子力学隧穿

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We investigate the quantum-mechanical tunneling between the "patterns" of the, so-called, associative neural networks. Being the relatively stable minima of the "configuration-energy" space of the networks, the "patterns" represent the macroscopically distinguishable states of the neutral nets. Therefore, the tunneling represents a macroscopic quantum effect, but with some special characteristics. Particularly, we investigate the tunneling between the minima of approximately equal depth, thus requiring no energy exchange. If there are at least a few such minima, the tunneling represents a sort of the "random walk" process, which implies the quantum fluctuations in the system, and therefore "malfunctioning" in the information processing of the nets. Due to the finite number of the minima, the "random walk" reduces to a dynamics modeled by the, so-called, Pauli master equation. With some plausible assumptions, the set(s) of the Pauli master equations can be analytically solved. This way comes the main result of this paper: the quantum fluctuations due to the quantum-mechanical tunneling can be "minimized" if the "pattern" - formation is such that there are mutually "distant" groups of the "patterns", thus providing the "zone" structure of the "pattern" formation. This qualitative result can be considered as a basis of the efficient deterministic functioning of the associative neural nets.
机译:我们研究了所谓的关联神经网络的“模式”之间的量子力学隧穿。作为网络“配置能量”空间的相对稳定的最小值,“模式”表示中性网络的宏观可区分状态。因此,隧穿代表了宏观量子效应,但是具有一些特殊的特性。特别地,我们研究了在近似相等深度的最小值之间的隧穿,因此不需要能量交换。如果至少有几个这样的最小值,则隧穿代表一种“随机游动”过程,这意味着系统中的量子涨落,从而在网络的信息处理中“失灵”。由于极小值的有限数目,“随机游动”简化为由所谓的Pauli主方程建模的动力学。通过一些合理的假设,可以解析地求解Pauli主方程组。这种方式是本文的主要结果:如果“模式”的形成使得“模式”存在相互“遥远”的组,则可以“最小化”由量子力学隧穿引起的量子涨落。 “模式”结构的“区域”结构。该定性结果可被视为关联神经网络有效确定性功能的基础。

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