Data-Driven Qubit Characterization and Optimal Control using Deep Learning
This work addresses the challenge of qubit control for quantum computing researchers, but it is incremental as it builds on existing machine learning methods applied to a specific quantum system.
The researchers tackled the problem of optimizing control pulses for high-fidelity quantum gates in quantum computing by proposing a machine learning protocol that uses a recurrent neural network to predict qubit behavior, enabling efficient gradient-based pulse optimization without a detailed system model, and demonstrated its effectiveness in simulations on a single ST0 qubit.
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.