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Quantum computing is poised to address NP-hard problems by markedly enhancing performance and broadening its range of applications. Despite present challenges with hardware, noise, and reliability, quantum algorithms, particularly in machine learning, are set to outperform classical approaches.
In Noisy Intermediate-Scale Quantum (NISQ) systems, segmenting circuits and utilizing distributed quantum systems effectively reduce noise and increase reliability. This method employs multiple quantum computers simultaneously, optimizing connections and scheduling for efficient circuit execution.
This dissertation explores NISQ quantum computing's potential in solving classical and quantum issues in areas like GANs, quantum chemistry, and computer vision. It emphasizes dynamic noise management, parallel system operation, and advanced scheduling to boost circuit fidelity. The study reviews quantum computing's superiority over classical methods for NP-hard problems, highlighting efficiency, reliability, and scalability.