QI 23: Quantum Control
Donnerstag, 21. März 2024, 09:30–13:00, HFT-FT 131
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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
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09:45 |
QI 23.2 |
Modelling two-qubit gates of superconducting transmon processors — Michael Krebsbach, •Martin Koppenhöfer, and Thomas Wellens
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10:00 |
QI 23.3 |
Qunatum Information Storage in Cavity Coupled Spin Ensembles — •Michael Schilling and Jószef Zsolt Bernád
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10:15 |
QI 23.4 |
Quantum Circuits Noise Tailoring from a Geometric Perspective — •Junkai Zeng, Yong-Ju Hai, Hao Liang, and Xiu-Hao Deng
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10:30 |
QI 23.5 |
Universal readout error mitigation — •Adrian S. Aasen, Andras Di Giovanni, Hannes Rotzinger, Alexey V. Ustinov, and Martin Gärttner
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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
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11:00 |
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15 min. break
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11:15 |
QI 23.7 |
Quantum gate design with machine learning — •Bijita Sarma and Michael Hartmann
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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
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11:45 |
QI 23.9 |
Accurate Quantum Feedback Control via Conditional State Tomography with Reinforcement Learning — •Sangkha Borah and Bijita Sarma
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12:00 |
QI 23.10 |
Quantum control landscapes of piecewise-constant pulses — •Martino Calzavara and Felix Motzoi
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12:15 |
QI 23.11 |
Deciding Observability in Quantum Dynamics Easily — Markus Wiener and •Thomas Schulte-Herbrüggen
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12:30 |
QI 23.12 |
Reinforcement learning entangling operations for spin qubits — •Mohammad Abedi
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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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