Abstract
Quantum computing has the potential to revolutionize modern computation and solve problems that classical computing systems are unable of solving, at least within a reasonable time frame. Nevertheless, for current quantum hardware whose performance and reliability are heavily impacted by noise. The subject of this research is the use of machine learning algorithms with quantum circuits to alleviate noise and enhance computational fidelity. Using data-driven models, the proposed method detects patterns of quantum errors and adjusts circuit structures, gate sequences, and quit mappings accordingly. These include supervised, reinforcement, and deep learning techniques in improving circuit accuracy as well as error rates. It proposes a hybrid optimization framework that combines classical ML algorithms with quantum execution and provides feedback mechanisms for continuous sampling of improved performance. Experimental results show that the fidelity of the optimized circuits and their noise, measured by average gate-fidelity is significantly better than a new set of heuristic methods based on machine learning.
The research also covers important topics such as small training data, hardware limitations and scalability. The results are indicative of the essential role that intelligent optimization will play in further developing noise-resilient quantum computing systems and hastening the transition to realistic, scalable quantum applications.
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