Noise Prediction and Reduction of Single Electron Spin by Deep-Learning-Enhanced Feedforward Control.

Area of Science:

• Quantum sensing
• Nanoscale physics
• Diamond-based technologies

Background:

• Noise-induced control imperfections limit diamond-based nanoscale sensing.
• Current measurement-based strategies are hindered by slow spin-state readout due to low photon-detection efficiency.
• This delay restricts real-time noise reduction performance.

Purpose of the Study:

• To introduce a deep learning approach for real-time noise prediction and compensation in quantum sensing.
• To overcome limitations imposed by slow spin-state readout in diamond-based sensors.
• To enhance the performance of nitrogen-vacancy (NV) centers in diamond for sensing applications.

Main Methods:

• Experimental implementation of feedforward quantum control for NV centers in diamond.
• Application of deep learning to predict noise trends and compensate for readout delays.
• Development of a theoretical model to explain the observed improvements.

Main Results:

• Significant enhancement of the electron spin decoherence time in NV centers.
• Improved sensing performance against noise through delay compensation.
• Demonstration of effective noise prediction and mitigation using deep learning.

Conclusions:

• Deep learning offers a viable solution to overcome readout delays in quantum sensing.
• The proposed scheme effectively protects spin coherence and enhances sensing capabilities.
• This approach is broadly applicable to various sensing schemes and quantum systems.