Data-Driven Qubit Characterization and Optimal Control using Deep Learning

TL;DR

This paper introduces a machine learning approach using recurrent neural networks to efficiently optimize control pulses for high-fidelity quantum gates without detailed system models, demonstrated through simulations on a single qubit.

Contribution

It presents a novel deep learning protocol for qubit control that bypasses complex system modeling by training RNNs on observed dynamics for pulse optimization.

Findings

Effective RNN-based qubit behavior prediction

Successful pulse optimization for high fidelity

Demonstrated on a single $ST_0$ qubit in simulations

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.