Michaela Eichinger - Quantum Machines

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Abstract: 
As quantum processors scale and edge closer to the thresholds required for fault-tolerant computation, the challenge of maintaining high-fidelity operations becomes ever more acute. Traditional calibration methods, while effective for small systems, are increasingly inadequate in the face of device drift and context-dependent noise such as crosstalk.
 
This talk explores how adaptive strategies are reshaping the landscape of quantum control and calibration of large quantum systems, shifting from static parameter maps to continuously optimized, autonomous pulse updates. Drawing on recent experimental results, we will look at specific examples of reinforcement-learning-based real-time system stabilisation to illustrate how such methodologies deliver rapid and robust optimization of multi-qubit circuits. Finally, I will discuss the integration of these adaptive techniques directly within quantum error correction frameworks and existing workflows.