Get Started
Home
Topics
Search
Library
Research questionHow can quantum algorithms accelerate sampling and optimization when oracle information is stochastic and chains need not be reversible?Sampling from a target distribution and using that process for optimization can require many oracle queries when gradients are stochastic or only noisy function values are available. The difficulty is retaining convergence guarantees without exact gradients or reversible Markov chains.
Machine Learning
Research Paper
Statistical Machine Learning
Technology
Latest papersRecent research connected to this question, newest first.Quantum Speedups for Sampling and Non-convex Optimization with Stochastic OraclesThe source considers finite-sum stochastic gradient oracles and noisy stochastic evaluation oracles, applied to Langevin and Hamiltonian Monte Carlo sampling. Its theoretical guarantees cover strongly log-concave and certain non-log-concave distributions satisfying a log-Sobolev inequality, with convergence measured in Wasserstein distance and Kullback–Leibler divergence; optimization implications include nonsmooth and approximately convex objectives. The evidence is theoretical query-complexity and convergence analysis rather than empirical deployment results.research paper · Sep 2, 2026
Related questions
How can quantum generative models reduce sequential circuit evaluations during sampling on NISQ hardware?How can quantum neural networks prevent simulator-era postprocessing from silently discarding hardware measurements?How can quantum developers track experiments and provenance reproducibly amid noisy hardware and repeated execution?What statistical guarantees can quantum maximum-likelihood prediction provide from finite iid samples?