171 citations · 480 across the 25 of their papers we have counts for
6 papers · 1 filter
Quantum Wasserstein Generative Adversarial Networks
Shouvanik Chakrabarti, Yiming Huang, Tongyang Li +2
The study of quantum generative models is well-motivated, not only because of its importance in quantum machine learning and quantum chemistry but also because of the perspective o…
Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning
Nai-Hui Chia, András Gilyén, Tongyang Li +3
We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang's breakthrough quant…
Quantum algorithm for estimating volumes of convex bodies
Shouvanik Chakrabarti, Andrew M. Childs, Shih-Han Hung +3
Estimating the volume of a convex body is a central problem in convex geometry and can be viewed as a continuous version of counting. We present a quantum algorithm that estimates…
Sublinear quantum algorithms for training linear and kernel-based classifiers
Tongyang Li, Shouvanik Chakrabarti, Xiaodi Wu
We investigate quantum algorithms for classification, a fundamental problem in machine learning, with provable guarantees. Given -dimensional data points, the state-of-the-a…
Distributional property testing in a quantum world
András Gilyén, Tongyang Li
A fundamental problem in statistics and learning theory is to test properties of distributions. We show that quantum computers can solve such problems with significant speed-ups. I…
Quantum-inspired sublinear algorithm for solving low-rank semidefinite programming
Nai-Hui Chia, Tongyang Li, Han-Hsuan Lin +1
Semidefinite programming (SDP) is a central topic in mathematical optimization with extensive studies on its efficient solvers. In this paper, we present a proof-of-principle subli…