Quantum computing holds the potential to solve problems that are intractable for classical systems. However, the physical realization of large-scale quantum systems remains a challenge due to the difficulty of scaling qubit counts. Distributed Quantum Computing (DQC) offers a promising solution by interconnecting multiple Quantum Processing Units (QPUs), effectively increasing the number of usable qubits. This interconnection introduces additional operations and exacerbates the complexity of qubit routing and entanglement management during circuit execution. While a reinforcement learning (RL) approach by Promponas et al. shows promise for qubit routing and EPR management in distributed quantum computing (DQC) environments, it suffers from inconsistent performance and is unable to reliably compile larger circuits. To address these limitations, we introduce a novel action space that allows direct operations between arbitrary qubit pairs, rather than restricting interactions to neighbouring qubits. While this significantly reduces solution depth, it increases the size of the action space, posing scalability challenges. To address this, we propose a novel neural network architecture that computes Q-values for qubit pairs based on the values of their individual qubits, considerably reducing the number of trainable parameters. Additionally, we extend the masking strategy to eliminate sub-optimal actions, effectively constraining the branching factor and accelerating learning. Together, these enhancements enable the agent to compile larger circuits with improved speed and consistency, significantly outperforming the baseline. Overall, our contributions result in better performance and reduced training time, marking a step forward in scalable quantum circuit compilation for distributed quantum systems.

The goal of this thesis is to develop a ROS package that facilitates the control and navigation of a Duckiebot robot. With the rise of robot swarms the need for autonomous charging system for robots is increasing. An implementation for decentralised autonomous behaviour for a Duckiebot for a wireless charging system in a Duckietown environment is discussed. The devised system is divided among three different modules and is implemented in ROS:
• An image recognition module;
• A navigation module;
• A motion control module;
The image recognition module uses linear image processing techniques and YOLO object detection in order to detect objects in images from the robots front facing camera. It detects traffic lights and road markings in order to tell the robot where to go.

The navigation module uses odometry to keep track of the robots current position. The odometry is reset in order to maintain accuracy. When the battery of the robot reaches a certain point the robot will decide to
charge. It will then initiate path finding using Lee’s algorithm in order to find a path to a charging park.

Finally the motion control processes all the information in order to drive the wheels of the robot.
The system is thought to be able to navigate to a charging station, charge and then leave the charging station using the designed ROS package.