Chaos Modeling: A Comparison of Classical and Quantum Reservoir Computer Capabilities
By Arjun Bhat, Kanjonavo Sabud
Dynamic systems have always been an integral part of our world. Computational models are being explored for their potential of modeling dynamic systems ranging from storm development and wildfire behavior to natural language understanding and stock market prediction. The development of an RNN subfield known as Reservoir Computing (RC) has received wide attention and is observed to be well suited for handling dynamic system forecasting. Likewise, quantum machine learning has also been seen to increase model capabilities. In this project, we researched on combining the two by implementing a Quantum Reservoir Computer (QRC) for modeling a benchmark dynamic system -- the Lorenz 1963. Furthermore, we used a standard Artificial Neural Network (ANN) as a control model. Our results showed that Classical RC with forecast horizons averaging 90% in train lengths of 9000 timesteps greatly outperformed the ANN model which averaged 30%. However, our QRC model completely failed to model the Lorenz 63 system and achieved a 0% forecast horizon. As research on QRC and its dynamic systems modeling capabilities is still in its infancy, it is highly likely that our implementation of QRC was not prepared to model a system as complex as Lorenz 63. Future work, therefore, includes improving our implementation of QRC. It would also be interesting to explore the intersection of graph theory and networks within the reservoir of a reservoir computer.