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A comprehensive review of Quantum Machine Learning: from NISQ to Fault Tolerance
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Quantum machine learning, which involves running machine learning algorithms on quantum devices, has garnered significant attention in both academic and business circles. In this paper, we offer a comprehensive and unbiased review of the various concepts that have emerged in the field of quantum machine learning. This includes techniques used in Noisy Intermediate-Scale Quantum (NISQ) technologies and approaches for algorithms compatible with fault-tolerant quantum computing hardware. Our review covers fundamental concepts, algorithms, and the statistical learning theory pertinent to quantum machine learning.
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Cited by 3 Pith papers
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Benchmarking Quantum Models for Time-series Forecasting
A benchmarking study comparing five quantum forecasting models with classical baselines on two real data sets, finding no quantum advantage over classical models.
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Data-Dependent Generalization Bounds for Parameterized Quantum Models Under Noise
A generalization bound for noisy parameterized quantum classifiers is derived from quantum Fisher information, parameter-space volume, and sample size, with local refinements claimed to tighten it.
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Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications
A review of supervised quantum machine learning techniques and a speculative roadmap for 2025-2035, concluding that practical quantum advantage will be confined to niche domains until fault-tolerant hardware arrives.
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