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The training of a parameterized model largely depends on the landscape of the underlying loss function.
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Edward Grant, Leonard Wossnig, Mateusz Ostaszewski, and Marcello Benedetti · 2019
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“Learning and inference on generative adversarial quantum circuits”
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“Escaping from the barren plateau via gaussian initializations in deep variational quantum circuits”
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“A generative modeling approach for benchmarking and training shallow quantum circuits”
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Kaining Zhang, Liu Liu, Min-Hsiu Hsieh, and Dacheng Tao · 2022
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“Observing ground-state properties of the fermi-hubbard model using a scalable algorithm on a quantum computer”
Stasja Stanisic et al · 2022
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“Equivalence of quantum barren plateaus to cost concentration and narrow gorges”
Andrew Arrasmith, Zoë Holmes, M Cerezo, and Patrick J Coles · 2022
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“Diagnosing Barren Plateaus with Tools from Quantum Optimal Control”
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“Synergistic pretraining of parametrized quantum circuits via tensor networks”
Manuel S. Rudolph, Jacob Miller, Danial Motlagh, et al · 2023
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https://qiskit.org/documentation/stubs/qiskit.circuit.library.RealAmplitudes.html (2023)
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