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Berlin 2024 – wissenschaftliches Programm

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QI: Fachverband Quanteninformation

QI 9: Quantum Machine Learning and Classical Simulability

QI 9.10: Vortrag

Dienstag, 19. März 2024, 12:15–12:30, HFT-FT 101

On the average-case complexity of learning output distributions of quantum circuitsAlexander Nietner1, Marios Ioannou1, Ryan Sweke1,3, Richard Kueng2, Jens Eisert1, •Marcel Hinsche1, and Jonas Haferkamp1,41FU Berlin — 2JKU Linz — 3IBM Quantum — 4Harvard University

In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model, which models most practical algorithms. Our main results are:

Moreover, we confirm a variant of a conjecture by Aaronson and Chen and show that the output distribution of a brickwork random quantum circuit is constantly far from any fixed distribution in total variation distance with probability 1−O(2n).

Keywords: Random Quantum Circuits; Learning Quantum States; Average Case Complexity

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