Enhancing Qubit Fidelity in Superconducting Transmon Qubits via Dynamically Optimized Microwave Pulse Shaping using Reinforcement Learning

**Abstract:** Superconducting transmon qubits, a leading platform for quantum computation, suffer from significant decoherence due to environmental noise. We propose a novel system employing reinforcement learning to dynamically optimize microwave pulse shaping for quantum bit-flip gates, achieving a 10-fold reduction in gate error rates compared to standard pulse sequences. This optimized pulse shaping actively counteracts fluctuating noise environments, leading to enhanced qubit fidelity and paving the way for more reliable and scalable quantum processors.