Quantum Routing using Value-Based Reinforcement Learning

Abstract

This thesis addresses the Quantum routing problem through the implementation of a reinforcement learning algorithm. Quantum routing is the problem of making quantum circuits executable on a quantum computer with limits of connectivity which requires requires swapping information between qubits. A value-based vari ant 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 study focused on smaller grid sizes of 3x2, 3x3, and 3x4. Due to time constraints we were not fully able to adequately access the performance of the model and were only able to achieve solutions for smaller models, while the results for the larger ones (3x3 and 4x3) were lackluster. For larger grid sizes our analysis on multiple hyper-parameters revealed a better understanding for the reasons for this, suggesting possible reme dies. 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.