Quantum Machine Learning for Enhanced Qubit Stability and Gate Efficiency: A Qiskit-based Framework

Abstract

The Noisy Intermediate-Scale Quantum (NISQ) era shows its practical applications through quantum computing which encounters essential problems with qubit stability and gate fidelity. This research introduces a complete quantum machine learning framework which uses IBM’s Qiskit toolkit to optimize quantum hardware performance through its predictive optimization and adaptive error correction systems. The proposed framework is based on Long Short-Term Memory (LSTM) networks for coherence time prediction, the Variational Quantum Eigensolver (VQE) to optimize gate performance, and Quantum Neural Networks (QNN) for error classification. Experiments conducted on IBM Quantum systems achieved significant performance enhancements which included an 18% increase in T1 coherence times, a 19% improvement in T2 times, and a 23% decrease in CNOT gate error rates. The decoherence predictor achieves 94.2% accuracy in forecasting coherence decay 300 microseconds in advance, while the QNN error classifier attains 91.7% accuracy across four error categories. The findings demonstrate that machine learning serves as an effective method to integrate existing NISQ systems with fault-tolerant quantum architectures, addressing the impact on algorithm development and error mitigation.