Adaptive denoising quantum state preparation in a dynamic environment

Quantum state preparation (QSP) is an essential step in many quantum information processing tasks and has been deeply studied in a closed system. However, environmental noise inevitably destroys the quantumness of the system, resulting in decreased fidelity. In this work, we investigate the enhancement of fidelity by pulse control for arbitrary single or two-qubit QSP in a noisy environment. The control pulses are designed by leveraging supervised learning. The environment is modeled as a collection of bosons, and we use the quantum state diffusion equation technique to deal with the dynamics of the system. We consider two types of noise: quasistatic noise and dynamic noise. We propose active and adaptive denoising schemes for the quasistatic and dynamic noise, respectively. The environmental parameters are required to construct the dataset when training the neural network (NN) in adaptive denoising, while they are not needed in active denoising. The adaptive denoising scheme is universal in that it can be effectively applied to both quasistatic and dynamic noises. High-fidelity QSP has been obtained by the well-trained NN. Our work demonstrates that machine learning has great potential in optimal pulse design in a dynamic environment.