Automated Optimization of Single-Qubit Fluxonium Transmon Control Pulses via Reinforcement Learning and HyperScore-Driven Validation

**Abstract:** This paper proposes a novel framework for optimizing control pulses for single-fluxonium transmons, a promising platform for superconducting quantum computing. Leveraging reinforcement learning (RL) and a HyperScore-based validation pipeline, our system autonomously and efficiently generates pulse sequences that achieve high-fidelity qubit state manipulation. This approach surpasses traditional optimization methods by dynamically adapting to device-specific characteristics and incorporating rigorous quality control metrics. The methodology is immediately deployable using current hardware and software stacks, demonstrating a path towards scalable and robust single-qubit control.