The aim of this project was to develop a new Reservoir Computer implementation, based on a chaotic Chua circuit. In addition to suitable classification and regression benchmarks, the Reservoir Computer was applied to Post-Quantum Cryptography, with its suitability for this application investigated and assessed. The cryptographic algorithm utilised was the Learning with Errors problem, for both encryption and decryption. To achieve this, the Chua circuit was characterised, in simulation, and by physical circuit testing. The Reservoir Computer was designed and implemented using the results of the characterisation. As part of this development, noise was considered and mitigated. The benchmarks demonstrate that the Reservoir Computer can achieve current literature benchmarks with low error. However, the results with Learning with Errors suggest that a Chua-based Reservoir Computer is not sufficiently complex to tackle the high non-linearity in Post-Quantum Cryptography. Future work would involve researching the use of different combinations of multiple Chua Reservoir Computers in larger neural network architectures. Such architectures may produce the required high-dimensional behaviour to achieve the Learning with Errors problem. This project is believed to be only the second instance of a Chua-based Reservoir Computer in academia, and it is the first to be applied to challenging real-world tasks such as Post-Quantum Cryptography. It is also original by its investigation of hitherto unexplored parameters, and their impact on performance. It demonstrates a proof-of-concept for a mass-producible, inexpensive, low-power consumption hardware neural network. It also enables the next stages in research to occur, paving the road for using Chua-based Reservoir Computers across various applications.

翻译：本项目旨在开发一种基于混沌蔡氏电路的新型储层计算实现方案。除了适用于分类和回归基准测试外，该储层计算被应用于后量子密码学领域，并对其适用性进行了研究与评估。所采用的密码算法为基于容错学习问题的加密与解密方案。为实现这一目标，通过仿真和物理电路测试对蔡氏电路进行了特性表征。基于表征结果，设计并实现了该储层计算系统。在此开发过程中，对噪声问题进行了考量与抑制。基准测试表明，该储层计算能够以较低误差达到当前文献报道的基准水平。然而，容错学习问题的实验结果表明，基于蔡氏电路的储层计算尚不足以应对后量子密码学中的高度非线性挑战。未来工作将研究在更大规模的神经网络架构中采用多个蔡氏储层计算单元的不同组合方式。此类架构可能产生解决容错学习问题所需的高维行为特性。据信，本项目是学术界第二个基于蔡氏电路的储层计算研究，也是首个将其应用于后量子密码学等现实挑战性任务的案例。其创新性还体现在对迄今未探索参数的考察及其对性能影响的系统性研究。本研究为可大规模生产、低成本、低功耗的硬件神经网络提供了概念验证，并为后续研究奠定了基础，为基于蔡氏电路的储层计算在各种应用中的推广铺平了道路。