ENHANCING QUANTUM ERROR CORRECTION: OPTIMIZING NOISE REDUCTION TECHNIQUES FOR RELIABLE QUANTUM COMPUTATION

Abstract

Quantum computing holds the potential to revolutionize computation by solving complex problems that classical
computers cannot. However, quantum systems are highly susceptible to errors due to noise and decoherence. In this paper, we
propose a novel hybrid AI-Quantum approach for Quantum Error Correction (QEC) to optimize noise reduction techniques.
By leveraging deep learning and reinforcement learning, we develop a method to predict and correct quantum noise in real time.
Our experiments, conducted on IBM Qiskit simulators and actual quantum processors, demonstrate a significant reduction in
quantum gate errors compared to traditional QEC codes. Our method achieves improved fault tolerance with reduced qubit
overhead, paving the way for scalable and reliable quantum computation. Furthermore, we make our research publicly available
with open-source Python code and an interactive Jupyter notebook, enabling others to replicate and extend our work.