High Fidelity Quantum Gates under random telegraph noise: Machine Learning Approach

Achieving high fidelity quantum gates under random telegraph noise (RTN) is of great interest for quantum computing. We have generated data for qubit driven by π, CORPSE, SCORPSE, symmetric and asymmetric pulses in presence of RTN and by using train-test model in machine learning algorithm within python environment, we report symmetric pulse provides large fidelity recovery against noise among all the other pulses for large noise correlation time, whereas π-pulse has small error among all the other pulses for small noise correlation time. Investigation of high fidelity quantum gate for small energy amplitudes of RTN may be useful for low temperature measurements, whereas large energy amplitudes of RTN may be useful for room temperature measurements of quantum error correction codes.