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@article{206997,
        author = {J PradeepKumar and Dr.Shifana Begum and Dr.K Venkata Nagendra},
        title = {A SURVEY OF MACHINE LEARNING METHODS IN THE QUANTUM STACK},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {3737-3740},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206997},
        abstract = {Quantum computing promises exponential speedups for specific computational problems but faces severe bottlenecks in qubit coherence, error rates, and control optimization. Machine Learning (ML) has emerged as a critical tool to accelerate the development of Noisy Intermediate-Scale Quantum (NISQ) and Fault-Tolerant Quantum (FTQ) systems. This paper surveys the intersection of ML and quantum hardware engineering. We categorize contemporary ML methodologies applied to quantum state estimation, error mitigation, and control optimization. Furthermore, we provide a comparative analysis of these methods against classical benchmarks and outline open challenges in data scaling and model interpretability.},
        keywords = {Quantum Computing, Machine Learning, Quantum Control Optimization, Quantum Error Correction (QEC), Quantum State Tomography (QST) and NISQ Devices},
        month = {July},
        }