Data-Driven Qubit Characterization and Optimal Control using Deep Learning

2026-01-26

AI Summary

This work addresses the challenges in realizing high-fidelity quantum gates, which stem from difficulties in control pulse optimization, complex system modeling, and gradient computation. To overcome these limitations, the authors propose a data-driven, model-free approach: random control pulses are applied to a qubit to collect its dynamical response, and a recurrent neural network (RNN) is trained to learn this behavior. Leveraging the differentiable nature of the learned model, gradient-based optimization is then employed to design high-fidelity control pulses. This method circumvents the need for an accurate physical model by integrating deep learning with optimal control theory, enabling efficient characterization and pulse design for quantum systems. Numerical simulations on a single ST₀ qubit demonstrate successful optimization of high-fidelity gate operations, confirming the efficacy and practicality of the proposed framework.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• quantum computing
• qubit control
• pulse optimization
• system dynamics
• gradient evaluation

Innovation

• data-driven
• deep learning
• quantum optimal control
• recurrent neural network
• qubit characterization