Master thesis presentation Mikkel Opperud, MPPHS

Abstract: This thesis addresses the Quantum routing problem through the implementation of a reinforcement learning algorithm. A value-based variant of the Q-learning algorithm, coupled with deep convolutional neural networks, was employed to optimize the routing process in a grid topology environment. The environment allowed the agent to place and remove swaps and to "pull back" any immediately executable qubits. The reward scheme was designed to optimize for a shortened circuit depth with the first layers of swaps not counted, thus solving the Quantum routing and placement problem concurrently. The results of this study were evaluated against the baseline algorithm provided by Qiskit, with a focus on smaller grid sizes of 3x2, 3x3, and 3x4. This comparative analysis revealed specific instances of agent errors, contributing to a better understanding of the challenges associated with this approach. In conclusion, while the algorithm encountered issues during the experiment, these obstacles present opportunities for future improvement and refinement. This research provides a foundation for future studies in the realm of Quantum routing, highlighting potential avenues for enhanced algorithm performance.

Password: 437892

Supervisore: Mats Granath
Examiner: Mats Granath
Opponent: Yinzi Xiao