15 citations · 71 across the 34 of their papers we have counts for
9 papers · 1 filter
The Complexity of NISQ
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang +1
The recent proliferation of NISQ devices has made it imperative to understand their computational power. In this work, we define and study the complexity class , wh…
Learning to predict arbitrary quantum processes
Hsin-Yuan Huang, Sitan Chen, John Preskill
We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process over qubits. For a wide range of distributions on…
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Sitan Chen, Sinho Chewi, Jerry Li +3
We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of la…
When Does Adaptivity Help for Quantum State Learning?
Sitan Chen, Brice Huang, Jerry Li +2
We consider the classic question of state tomography: given copies of an unknown quantum state , output which is close to in some sense,…
Learning (Very) Simple Generative Models Is Hard
Sitan Chen, Jerry Li, Yuanzhi Li
Motivated by the recent empirical successes of deep generative models, we study the computational complexity of the following unsupervised learning problem. For an unknown neural n…
Learning Polynomial Transformations
Sitan Chen, Jerry Li, Yuanzhi Li +1
We consider the problem of learning high dimensional polynomial transformations of Gaussians. Given samples of the form , where is hidden and $p:…