



@article {3150,
	title = {Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants},
	journal = {Advances in Neural Information Processing Systems (NeurIPS 2022)},
	volume = {35},
	year = {2022},
	month = {10/12/2022},
	keywords = {FOS: Computer and information sciences, FOS: Mathematics, FOS: Physical sciences, Machine Learning (cs.LG), Optimization and Control (math.OC), Quantum Physics (quant-ph)},
	doi = {10.48550/ARXIV.2210.06539},
	url = {https://arxiv.org/abs/2210.06539},
	author = {Andrew M. Childs and Li, Tongyang and Liu, Jin-Peng and Wang, Chunhao and Zhang, Ruizhe}
}
@article {2878,
	title = {Quantum Query Complexity with Matrix-Vector Products},
	journal = {48th International Colloquium on Automata, Languages, and Programming (ICALP 2021)},
	year = {2021},
	month = {2/7/2021},
	publisher = {Schloss Dagstuhl {\textendash} Leibniz-Zentrum f{\"u}r Informatik},
	address = {Dagstuhl, Germany},
	abstract = {<p>We study quantum algorithms that learn properties of a matrix using queries that return its action on an input vector. We show that for various problems, including computing the trace, determinant, or rank of a matrix or solving a linear system that it specifies, quantum computers do not provide an asymptotic speedup over classical computation. On the other hand, we show that for some problems, such as computing the parities of rows or columns or deciding if there are two identical rows or columns, quantum computers provide exponential speedup. We demonstrate this by showing equivalence between models that provide matrix-vector products, vector-matrix products, and vector-matrix-vector products, whereas the power of these models can vary significantly for classical computation.</p>},
	isbn = {978-3-95977-195-5},
	doi = {10.4230/LIPIcs.ICALP.2021.55},
	url = {https://drops.dagstuhl.de/opus/volltexte/2021/14124},
	author = {Andrew M. Childs and Hung, Shih-Han and Li, Tongyang}
}
