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Berlin 2024 – wissenschaftliches Programm

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QI: Fachverband Quanteninformation

QI 23: Quantum Control

Donnerstag, 21. März 2024, 09:30–13:00, HFT-FT 131

09:30 QI 23.1 Neural-network-supported preparation of cat states in Jaynes-Cummings model — •Pavlo Bilous, Hector Hutin, Benjamin Huard, and Florian Marquardt
09:45 QI 23.2 Modelling two-qubit gates of superconducting transmon processorsMichael Krebsbach, •Martin Koppenhöfer, and Thomas Wellens
10:00 QI 23.3 Qunatum Information Storage in Cavity Coupled Spin Ensembles — •Michael Schilling and Jószef Zsolt Bernád
10:15 QI 23.4 Quantum Circuits Noise Tailoring from a Geometric Perspective — •Junkai Zeng, Yong-Ju Hai, Hao Liang, and Xiu-Hao Deng
10:30 QI 23.5 Universal readout error mitigation — •Adrian S. Aasen, Andras Di Giovanni, Hannes Rotzinger, Alexey V. Ustinov, and Martin Gärttner
10:45 QI 23.6 Benchmarking a readout noise mitigation method on a superconducting qubit — •Andras Di Giovanni, Adrian S. Aasen, Hannes Rotzinger, Martin Gärttner, and Alexey V. Ustinov
  11:00 15 min. break
11:15 QI 23.7 Quantum gate design with machine learning — •Bijita Sarma and Michael Hartmann
11:30 QI 23.8 Robust quantum gates for dynamical correction of coherent errors — •Xiu-Hao Deng, Yong-Ju Hai, Yuanzhen Chen, and Kangyuan Yi
11:45 QI 23.9 Accurate Quantum Feedback Control via Conditional State Tomography with Reinforcement Learning — •Sangkha Borah and Bijita Sarma
12:00 QI 23.10 Quantum control landscapes of piecewise-constant pulses — •Martino Calzavara and Felix Motzoi
12:15 QI 23.11 Deciding Observability in Quantum Dynamics EasilyMarkus Wiener and •Thomas Schulte-Herbrüggen
12:30 QI 23.12 Reinforcement learning entangling operations for spin qubits — •Mohammad Abedi
12:45 QI 23.13 Improving robustness of quantum feedback control with reinforcement learning — •Manuel Guatto, Francesco Ticozzi und Gian Antonio Susto
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