聚合忆阻器作为概率位生成的熵源

Stephen H. Foulger, Yuriy Bandera, Igor Luzinov, Travis Wanless
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引用次数: 0

摘要

概率比特或p比特代表了一种新的计算元素,它弥补了量子计算中使用的确定性经典比特和量子比特之间的差距。与保持0或1的确定状态的经典比特不同,p位以受控的随机方式在这些状态之间波动。这种概率行为允许以一种利用固有随机性的形式来表示和处理信息。在这项研究中,提出了一种独特的方法来产生p位使用杂化共轭聚合物,聚4-((6-(4h -二噻吩[3,2-b:2',3'-d]吡咯-4-基)己基)氧)- n, n -二苯基苯胺(pTPADTP),作为记忆材料。聚合物的共轭主链与垂坠的三苯胺基团相结合,可以通过随机电阻开关产生p位。这种聚合物忆阻器的随机性使其特别适合于随机优化、概率算法和人工神经网络中的p位应用。两种协同渗透机制促进了聚合物中的电荷传输:一种是沿着聚合物主链发生的,另一种是通过悬垂的三苯胺基团发生的。pTPADTP生成的p位的研究为推进计算的理论和实践开辟了新的途径,其中不确定性和随机性被利用为宝贵的计算资源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Polymeric Memristors as Entropy Sources for Probabilistic Bit Generation

Polymeric Memristors as Entropy Sources for Probabilistic Bit Generation

Probabilistic bits, or p-bits, represent a novel computational element that bridges the gap between deterministic classical bits and quantum bits (qubits) used in quantum computing. Unlike classical bits that maintain a definite state of 0 or 1, a p-bit fluctuates between these states in a controlled, stochastic manner. This probabilistic behavior allows for the representation and processing of information in a form that leverages inherent randomness. In this study, a unique approach is presented to generating p-bits using a hybrid conjugated polymer, poly-4-((6-(4H-dithieno[3,2-b:2',3'-d]pyrrol-4-yl)hexyl)oxy)-N,N-diphenylaniline (pTPADTP), as a memristive material. The polymer's conjugated backbone, combined with pendant triphenylamine groups, enables the creation of p-bits through random resistance switching. The stochasticity of this polymeric memristor makes it particularly suited for p-bit applications in stochastic optimization, probabilistic algorithms, and artificial neural networks. The charge transport in the polymer is facilitated by two synergistic percolation mechanisms: one occurring along the polymer backbone and the other through the pendant triphenylamine groups. The study of p-bits generated from pTPADTP opens new avenues for advancing both the theory and practice of computation, where uncertainty and randomness are harnessed as valuable computational resources.

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