PIRSA:23020031

Video URL

https://pirsa.org/23020031

Learning to predict arbitrary quantum processes

Huang, H. (2023). Learning to predict arbitrary quantum processes. Perimeter Institute for Theoretical Physics. https://pirsa.org/23020031

Huang, Hsin-Yuan. Learning to predict arbitrary quantum processes. Perimeter Institute for Theoretical Physics, Feb. 02, 2023, https://pirsa.org/23020031 

          @misc{ scivideos_PIRSA:23020031,
            doi = {10.48660/23020031},
            url = {https://pirsa.org/23020031},
            author = {Huang, Hsin-Yuan},
            keywords = {Quantum Information},
            language = {en},
            title = {Learning to predict arbitrary quantum processes},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2023},
            month = {feb},
            note = {PIRSA:23020031 see, \url{https://scivideos.org/index.php/pirsa/23020031}}
          }

Hsin-Yuan Huang California Institute of Technology (Caltech)

Talk numberPIRSA:23020031
DOI10.48660/23020031
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process over n qubits. For a wide range of distributions D on arbitrary n-qubit states, we show that this ML algorithm can learn to predict any local property of the output from the unknown process, with a small average error over input states drawn from D. The ML algorithm is computationally efficient even when the unknown process is a quantum circuit with exponentially many gates. Our algorithm combines efficient procedures for learning properties of an unknown state and for learning a low-degree approximation to an unknown observable. The analysis hinges on proving new norm inequalities, including a quantum analogue of the classical Bohnenblust-Hille inequality, which we derive by giving an improved algorithm for optimizing local Hamiltonians. Overall, our results highlight the potential for ML models to predict the output of complex quantum dynamics much faster than the time needed to run the process itself.

Zoom link:  https://pitp.zoom.us/j/93857777354?pwd=c044blZuQVhLS200ME4vN25uaGJudz09