Fast High-Fidelity Readout of a Single Trapped-Ion Qubit via Machine-Learning Methods
Zihan Ding, Jin‐Ming Cui, Yun‐Feng Huang, Chuan‐Feng Li, Tao Tu, Guang‐Can Guo · Physical Review Applied · 2019
The accuracy and speed of qubit readout can greatly affect the performance of quantum computers, which are held back by the lack of a more adaptive, accurate method for determining the system's quantum state. This study uses field-programmable gate arrays for machine-learning-assisted methods of single-qubit readout on a Yb${}^{+}$ ion-trap system, achieving 99.53% average fidelity within 171 $\ensuremath{\mu}$s per sample. The proposed scheme shows considerable advantages over traditional methods in fidelity, speed, and robustness, and is compatible with real-time readout and feedback control of qubit states.