Speaker
Description
Tensor networks connect quantum many-body physics, machine learning, and quantum computing through compact representations of states, probability distributions, and learning models. In this talk, we explore how these representations support learning and sampling, and how they can mediate between classical computation and quantum circuits.
We discuss tensor-network approaches to generative modeling, including Born machines and sampling from matrix product states, before turning to the embedding of classically trained models into quantum circuits. Particular attention is given to quantum state preparation, the overhead of postselection, and adiabatic encoding strategies for constructing quantum classifiers. Learning from quantum data is illustrated through tensor-network tomography and Bayesian inference of noise models from error-correction syndromes.
We also introduce tensor-network Monte Carlo as an approach to combining compression with stochastic sampling for error control. These examples motivate a discussion of when quantum processing can add value to classical tensor-network methods. We conclude with prospects for workflows that integrate classical learning, quantum state preparation, and quantum processing, with attention to computational cost, trainability, and accuracy.