Machine Learning-Based Noise Prediction for Qubit Stabilization

Qubits are very sensitive and easily get affected by noise from the surroundings, which causes their state to change or get lost. This problem reduces the stability of quantum operations. In this paper, we use Machine Learning to predict the noise before it affects the qubit. We collect data such as coherence time (T/T), gate error rate, readout values, and temperature changes from the quantum system. Using this data, we train models like LSTM to learn the noise pattern and forecast when noise will increase. When a noise change is predicted, the system automatically applies corrective actions like pulse adjustment or error mitigation to keep the qubit stable. The results show that this method helps to maintain qubit stability for a longer time and reduces error in quantum operations. This approach does not require extra qubits, so it is suitable for near-term quantum computers.