6 papers
The log log jam in Gaussian state tomography
Sitan Chen, Weiyuan Gong, Qi Ye +1
Unlike in finite dimensions, quantum information in continuous-variable systems has the peculiar feature that without imposing physical constraints, the sample complexity of state…
Computation-Utility-Privacy Tradeoffs in Bayesian Estimation
Sitan Chen, Jingqiu Ding, Mahbod Majid +1
Bayesian methods lie at the heart of modern data science and provide a powerful scaffolding for estimation in data-constrained settings and principled quantification and propagatio…
Optimal Inference Schedules for Masked Diffusion Models
Sitan Chen, Kevin Cong, Jerry Li
A major bottleneck of standard auto-regressive large language models is that their inference process is inherently sequential, resulting in very long and costly inference times. To…
S4S: Solving for a Diffusion Model Solver
Eric Frankel, Sitan Chen, Jerry Li +3
Diffusion models (DMs) create samples from a data distribution by starting from random noise and iteratively solving a reverse-time ordinary differential equation (ODE). Because ea…
Adaptivity can help exponentially for shadow tomography
Sitan Chen, Weiyuan Gong, Zhihan Zhang
In recent years there has been significant interest in understanding the statistical complexity of learning from quantum data under the constraint that one can only make unentangle…
Predicting quantum channels over general product distributions
Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh +3
We investigate the problem of predicting the output behavior of unknown quantum channels. Given query access to an -qubit channel and an observable , we aim to learn the…