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09:30 |
QI 9.1 |
Hauptvortrag:
Does provable absence of barren plateaus imply classical simulability? Or, why we might need to rethink variational quantum computing — •Zoe Holmes
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10:00 |
QI 9.2 |
Can a neural network fake a Boson Sampler? — •Martina Jung, Martin Gärttner, and Moritz Reh
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10:15 |
QI 9.3 |
Parametrized Quantum Circuits and their approximation capacities in the context of quantum machine learning — Alberto Manzano, •David Dechant, Jordi Tura, and Vedran Dunjko
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10:30 |
QI 9.4 |
Unifying (Quantum) Statistical and Parametrized (Quantum) Algorithms — •Alexander Nietner
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10:45 |
QI 9.5 |
Information-theoretic generalization bounds for learning from quantum data — •Matthias C. Caro, Tom Gur, Cambyse Rouzé, Daniel Stilck França, and Sathyawageeswar Subramanian
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11:00 |
QI 9.6 |
Efficient classical surrogate simulation of quantum circuits — •Manuel S. Rudolph, Enrico Fontana, Ross Duncan, Ivan Rungger, Zoë Holmes, Lukasz Cincio, and Cristina Cîrstoiu
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11:15 |
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15 min. break
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11:30 |
QI 9.7 |
Exponential concentration in quantum kernel methods — •Supanut Thanasilp, Samson Wang, Marco Cerezo, and Zoe Holmes
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11:45 |
QI 9.8 |
On the expressivity of embedding quantum kernels — •Elies Gil-Fuster, Jens Eisert, and Vedran Dunjko
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12:00 |
QI 9.9 |
A Multi-Excitation Projective Simulation Learning Agent — •Philip LeMaitre, Marius Krumm, and Hans Briegel
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12:15 |
QI 9.10 |
On the average-case complexity of learning output distributions of quantum circuits — Alexander Nietner, Marios Ioannou, Ryan Sweke, Richard Kueng, Jens Eisert, •Marcel Hinsche, and Jonas Haferkamp
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12:30 |
QI 9.11 |
Understanding quantum machine learning also requires rethinking generalization — •Elies Gil-Fuster, Jens Eisert, and Carlos Bravo-Prieto
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12:45 |
QI 9.12 |
More efficient exchange-only quantum gates via reinforcement learning — •Violeta N. Ivanova-Rohling, Niklas Rohling, and Guido Burkard
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13:00 |
QI 9.13 |
The Mean King’s Problem as a learning task — •Niklas Rohling
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