Quantum computing holds the potential to revolutionize computation by leveraging quantum mechanical principles to solve problems intractable for classical computers.
However, current noisy intermediate-scale quantum (NISQ) devices are constrained by limited qubit connectivity, high gate error rates, and hardware-specific noise patterns.
One of the key challenges in quantum circuit execution is qubit routing—efficiently mapping logical qubits to physical qubits while adhering to connectivity constraints and minimizing error accumulation.
Traditional heuristic and rule-based transpilation techniques struggle to generalize across different hardware architectures and noise conditions, motivating the exploration of machine learning approaches for more adaptive and scalable routing strategies.

This study investigates reinforcement learning (RL)-based methods for qubit routing, focusing on how different RL formulations (primitive vs.
hierarchical action spaces) and environment configurations (e.g., lookahead depth, training-circuit sizes) impact routing performance.
The designed RL environment models quantum hardware constraints through coupling graphs and noise parameters, providing configurable state representations and flexible action spaces.

Key findings indicate that a moderate lookahead (e.g., 4 gates) offers the best balance between performance and computational complexity.
Training on circuits with 8–16 gates yields optimal generalization to larger circuits.
Comparing RL policy formulations, hierarchical approaches converge faster and perform robustly on complex topologies, while primitive approaches occasionally achieve higher reliability given sufficient training.
Larger hardware graphs diminish RL’s relative advantage over heuristic transpilers, whereas higher-connectivity topologies improve RL routing efficiency.

Benchmarks against Qiskit’s standard transpilers reveal that RL methods are competitive on small-scale and highly connected devices but face challenges on larger and more constrained architectures.
While RL-based qubit routing can effectively integrate hardware noise and connectivity constraints, scalability and adaptability to dynamic error rates remain open challenges.

Future research should explore integrating more comprehensive noise models, adaptive noise tracking, concurrent gate execution, hybrid heuristic–RL approaches, and benchmarking with real hardware implementations.
As quantum devices scale, RL-driven, noise-aware compilation strategies may become integral to optimizing practical quantum computations.

Many people worldwide suffer from epileptic seizures and not all of them can be prevented using medicines, this thesis is being done for seizure prevention. This is based on implementing a medical body area network (MBAN) that takes sensory recordings across the whole body. This is needed as the research group that proposes this project found from previous research that this is needed for timely seizure prevention. In this report the design, implementation and results of building a prototype MBAN using Bluetooth low energy (BLE) is discussed. The result is a prototype that can measure heart rate when Bluetooth is turned off, but does function with Bluetooth when the heart rate sensor is replaced with mock data. Recommendations are made as to how to resolve the issues that arose during the implementation and as to which topologies should be implemented in the future.