# Data-Driven Qubit Characterization and Optimal Control using Deep Learning

arXiv: https://arxiv.org/abs/2601.18704 · HTML: https://mlsanity.com/paper/2601.18704/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 26 Jan 2026.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Model-free Quantum Gate Design and Calibration using Deep Reinforcement Learning](https://mlsanity.com/paper/2302.02371/) (2023)
- [Quantum Geometric Machine Learning for Quantum Circuits and Control](https://mlsanity.com/paper/2006.11332/) (2020)
- [Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach](https://mlsanity.com/paper/1511.08862/) (2015)
- [Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory](https://mlsanity.com/paper/1604.00279/) (2016)

## Abstract (arXiv)

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 ST0 qubit.