Abstract

This research delves into the dynamic intersection of artificial  intelligence (AI), machine learning (ML), and quantum computing, exploring their collaborative potential and contributions. The proposed method, centered around the fusion of reinforcement learning for quantum calibration, quantum error correction, and variational quantum algorithms, emerges as a groundbreaking approach with transformative implications. The autonomy introduced by reinforcement learning is a cornerstone, offering an innovative paradigm for quantum calibration. Through intelligent agents adapting quantum parameters autonomously, the proposed method not only expedites calibration processes but also mitigates the risks associated with manual interventions, ensuring a more robust and reliable quantum processor. This autonomous adaptation leads to improved stability and precision, setting a new standard in quantum computing methodology. Quantum error correction, another critical facet of the proposed method, addresses the inherent vulnerabilities of quantum systems. Stabilizer codes are employed to detect and correct errors, fortifying the reliability of quantum computations. This feature is paramount for the practical implementation of quantum computing applications, where the fragility of quantum states poses a considerable challenge. Variational quantum algorithms contribute to the efficiency and adaptability of the proposed method. By iteratively refining quantum parameters through classical optimization, these algorithms ensure that quantum circuits are optimized for diverse applications, spanning optimization problems and machine learning tasks. Comparative analyses against traditional methods underscore the proposed method’s superiority across autonomy, error resilience, calibration time, stability, efficiency, and reliability. This comprehensive advantage positions the proposed method as a frontrunner in the evolution of quantum computing methodologies.

Keywords: Algorithm, Artificial intelligence, Data mining, Machine learning, Neural networks, Optimization, Quantum computing, Statistical machine translation, Support vector machine, Variational algorithms