Dresden 2026 – scientific programme
Parts | Days | Selection | Search | Updates | Downloads | Help
QI: Fachverband Quanteninformation
QI 2: Implementations I
QI 2.1: Invited Talk
Monday, March 9, 2026, 09:30–10:00, BEY/0245
Advances in Frequency-Multiplexed Readout and Subsequent Qubit-State Reset — •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
In scalable, resource-efficient quantum processors with large numbers of superconducting qubits, readout performance often becomes a key limitation for overall system fidelity. Achieving fast, high-fidelity simultaneous measurement—critical for quantum error correction—typically relies on frequency-multiplexed readout to reduce resource overhead. However, crosstalk and other nonidealities pose significant challenges for conventional signal processing and state discrimination. Emerging machine learning (ML) approaches provide efficient, low-complexity mappings from measurement signals to qubit states, reducing error rates and enabling real-time scalability. In this talk, I will present recent advances in ML-based readout and reset techniques, along with their implementation on dedicated hardware. By combining scalable algorithms with compact, ML-driven discriminators deployed on FPGAs, we can address the readout bottleneck and substantially improve both fidelity and speed.
* 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; FPGA; Machine Learning