Data-Driven Qubit Characterization and Optimal Control using Deep Learning

arXiv (Cornell University)

Abstract

Quantum computing requires the optimization of control pulses to achieve high-fidelity quantum gates. We propose a machine learning-based protocol to address the challenges of evaluating gradients and modeling complex system dynamics. By training a recurrent neural network (RNN) to predict qubit behavior, our approach enables efficient gradient-based pulse optimization without the need for a detailed system model. First, we sample qubit dynamics using random control pulses with weak prior assumptions. We then train the RNN on the system's observed responses, and use the trained model to optimize high-fidelity control pulses. We demonstrate the effectiveness of this approach through simulations on a single $ST_0$ qubit.

Authors 5

1. Paul Surrey

   Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   JARA-FIT Institute for Quantum Information , Forschungszentrum Jülich GmbH and

2. Julian D. Teske

   Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   JARA-FIT Institute for Quantum Information , Forschungszentrum Jülich GmbH and

3. Tobias Hangleiter Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , 52074 Aachen , Germany

4. Hendrik Bluhm Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , 52074 Aachen , Germany

5. Pascal Cerfontaine

   TH Köln - University of Applied Sciences

   Affiliation as printed

   Institute of Computer and Communication Technology , TH Köln , 50679 Köln , Germany