@article {2501, title = {Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning}, journal = {to appear in Proceedings of STOC 2020}, year = {2020}, month = {6/18/2020}, abstract = {

We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang\&$\#$39;s breakthrough quantum-inspired algorithm for recommendation systems [STOC\&$\#$39;19]. Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gily{\'e}n et al. [STOC\&$\#$39;19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions. Our results give compelling evidence that in the corresponding QRAM data structure input model, quantum SVT does not yield exponential quantum speedups. Since the quantum SVT framework generalizes essentially all known techniques for quantum linear algebra, our results, combined with sampling lemmas from previous work, suffice to generalize all recent results about dequantizing quantum machine learning algorithms. In particular, our classical SVT framework recovers and often improves the dequantization results on recommendation systems, principal component analysis, supervised clustering, support vector machines, low-rank regression, and semidefinite program solving. We also give additional dequantization results on low-rank Hamiltonian simulation and discriminant analysis. Our improvements come from identifying the key feature of the quantum-inspired input model that is at the core of all prior quantum-inspired results: l2-norm sampling can approximate matrix products in time independent of their dimension. We reduce all our main results to this fact, making our exposition concise, self-contained, and intuitive.

}, doi = {https://doi.org/10.1145/3357713.3384314}, url = {https://arxiv.org/abs/1910.06151}, author = {Nai-Hui Chia and Andras Gilyen and Tongyang Li and Han-Hsuan Lin and Ewin Tang and Chunhao Wang} } @article {2722, title = {Sublinear classical and quantum algorithms for general matrix games}, journal = {To appear in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021)}, year = {2020}, month = {12/11/2020}, abstract = {

We investigate sublinear classical and quantum algorithms for matrix games, a fundamental problem in optimization and machine learning, with provable guarantees. Given a matrix A\∈Rn\×d, sublinear algorithms for the matrix game minx\∈Xmaxy\∈Yy⊤Ax were previously known only for two special cases: (1) Y being the l1-norm unit ball, and (2) X being either the l1- or the l2-norm unit ball. We give a sublinear classical algorithm that can interpolate smoothly between these two cases: for any fixed q\∈(1,2], we solve the matrix game where X is a lq-norm unit ball within additive error ε in time O~((n+d)/ε2). We also provide a corresponding sublinear quantum algorithm that solves the same task in time O~((n\−\−\√+d\−\−\√)poly(1/ε)) with a quadratic improvement in both n and d. Both our classical and quantum algorithms are optimal in the dimension parameters n and d up to poly-logarithmic factors. Finally, we propose sublinear classical and quantum algorithms for the approximate Carath{\'e}odory problem and the lq-margin support vector machines as applications.

}, url = {https://arxiv.org/abs/2012.06519}, author = {Tongyang Li and Chunhao Wang and Shouvanik Chakrabarti and Xiaodi Wu} } @article {2376, title = {Sublinear quantum algorithms for training linear and kernel-based classifiers}, journal = {Proceedings of the 36th International Conference on Machine Learning (ICML 2019) PMLR }, volume = {97}, year = {2019}, month = {04/03/2019}, pages = {3815-3824}, abstract = {

We investigate quantum algorithms for classification, a fundamental problem in machine learning, with provable guarantees. Given n d-dimensional data points, the state-of-the-art (and optimal) classical algorithm for training classifiers with constant margin runs in O~(n+d) time. We design sublinear quantum algorithms for the same task running in O~(n\−\−\√+d\−\−\√) time, a quadratic improvement in both n and d. Moreover, our algorithms use the standard quantization of the classical input and generate the same classical output, suggesting minimal overheads when used as subroutines for end-to-end applications. We also demonstrate a tight lower bound (up to poly-log factors) and discuss the possibility of implementation on near-term quantum machines. As a side result, we also give sublinear quantum algorithms for approximating the equilibria of n-dimensional matrix zero-sum games with optimal complexity Θ~(n\−\−\√).\ 

}, url = {https://arxiv.org/abs/1904.02276}, author = {Tongyang Li and Shouvanik Chakrabarti and Xiaodi Wu} }