Contribution of Artificial Intelligence and Machine Learning in Development of Quantum Computing

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.