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Statistical Estimation in the Spiked Tensor Model via the Quantum Approximate Optimization Algorithm
March 5, 2024, 2:46 p.m. | Leo Zhou, Joao Basso, Song Mei
stat.ML updates on arXiv.org arxiv.org
Abstract: The quantum approximate optimization algorithm (QAOA) is a general-purpose algorithm for combinatorial optimization. In this paper, we analyze the performance of the QAOA on a statistical estimation problem, namely, the spiked tensor model, which exhibits a statistical-computational gap classically. We prove that the weak recovery threshold of $1$-step QAOA matches that of $1$-step tensor power iteration. Additional heuristic calculations suggest that the weak recovery threshold of $p$-step QAOA matches that of $p$-step tensor power iteration …
abstract algorithm analyze arxiv computational cs.ds gap general math.pr math.st optimization paper performance prove quant-ph quantum statistical stat.ml stat.th tensor type via
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