Automated calibration of optimal control pulses on a superconducting quantum RAM (QRAM) device

Using optimal control techniques from robotics, we present an in situ calibration method for optimizing control pulses on superconducting quantum hardware. Our approach—quantum iterative learning control (QILC)—is a model-based, closed-loop method designed to achieve higher sample efficiency than traditional black-box methods. QILC aims to address noise, model mismatch errors, and signal distortions through a novel combination of error amplification experiment design, feedback, and optimal control. This method shows promise in enhancing operational fidelity on superconducting quantum switch devices, potentially advancing the experimental realization of quantum RAM (QRAM) pulse designs and offering broader applications across quantum computing.