Learning to Predict Arbitrary Quantum Processes - IPAM at UCLA

Explore an efficient machine learning algorithm for predicting unknown quantum processes over n qubits in this 50-minute lecture presented by Hsin-Yuan (Robert) Huang from the California Institute of Technology. Delve into the algorithm's ability to learn and predict local properties of output from unknown processes with small average errors, even for quantum circuits with exponentially many gates. Discover how the algorithm combines efficient procedures for learning properties of unknown states and low-degree approximations of unknown observables. Examine the proof analysis, including a quantum analogue of the classical Bohnenblust-Hille inequality, and its application in optimizing local Hamiltonians. Review numerical experiments demonstrating the algorithm's effectiveness in predicting quantum dynamics for large-scale systems. Gain insights into the potential of machine learning models to predict complex quantum dynamics faster than running the actual processes.