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Q: Fachverband Quantenoptik und Photonik
Q 21: Quantum Computing and Simulation III
Q 21.1: Hauptvortrag
Dienstag, 3. März 2026, 11:00–11:30, P 10
Interfacing with Quantum Information Processors—From Readout to Control — •Benjamin Lienhard1,2, Shivang Arora1,2, Emily Guo1,2, Priyanka Yashwantrao1,2, Patryk Dabkowski1,2,3, and Stefan Filipp1,2 — 1Technical University of Munich, Garching 85748, Germany — 2Walther-Meißner-Institut, Garching 85748, Germany — 3Zurich Instruments, 8005 Zürich, Switzerland
Balancing the effort required for controlling quantum systems—especially during characterization and calibration—is essential for making quantum computing practical. This effort must remain lightweight enough to track drifting system parameters, yet efficient enough to enable rapid recalibration. Although theoretical models offer valuable intuition, they often fail to capture the full complexity of real devices. Conversely, exhaustive system characterization can yield accurate numerical models, but it is typically too resource-intensive to scale. Model-free learning approaches provide a flexible, data-driven alternative; however, they also require substantial measurement overhead. As quantum processors continue to grow, these challenges intensify. In this presentation, I will introduce machine-learning-based protocols that we have developed to enhance superconducting qubit readout, as well as strategies for scalable calibration of large-scale quantum processors.
* funded through the EQuIPS Quantum Futur project from the Federal Ministry of Education and Research (BMBFTR) under funding number 13N17232.
Keywords: Superconducting Qubits; Qubit Readout; Qubit Control; Machine Learning