Learning to predict arbitrary quantum processes

arXiv2210.14894MaRDI QIDQ6415224

Author name not available (Why is that?)

Publication date: 26 October 2022

Abstract: We present an efficient machine learning (ML) algorithm for predicting any unknown quantum process $mathcalE$ over $n$ qubits. For a wide range of distributions $mathcalD$ 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~$mathcalE$, with a small average error over input states drawn from $mathcalD$. 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. Numerical experiments on predicting quantum dynamics with evolution time up to $10^6$ and system size up to $50$ qubits corroborate our proof. 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.

Has companion code repository: https://github.com/hsinyuan-huang/learning-quantum-process

This page was built for publication: Learning to predict arbitrary quantum processes

Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6415224)