  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.
