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quant-ph2025
Stochastic noise can be helpful for variational quantum algorithms
Junyu Liu, Frederik Wilde, Antonio Anna Mele +3
Saddle points constitute a crucial challenge for first-order gradient descent algorithms. In notions of classical machine learning, they are avoided for example by means of stochas…
quant-ph2024
Tight bounds on Pauli channel learning without entanglement
Senrui Chen, Changhun Oh, Sisi Zhou +2
Quantum entanglement is a crucial resource for learning properties from nature, but a precise characterization of its advantage can be challenging. In this work, we consider learni…
quant-ph2024
Exploring Shallow-Depth Boson Sampling: Towards Scalable Quantum Supremacy
Byeongseon Go, Changhun Oh, Liang Jiang +1
Boson sampling is a sampling task proven to be hard to simulate efficiently using classical computers under plausible assumptions, which makes it an appealing candidate for quantum…