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Quantum PAC learning sample complexity advantage requires inverse state-preparation access

By PulseAugur Editorial · [1 sources] ·

A new paper explores the sample complexity advantages of quantum PAC learning, specifically investigating whether quantum computation can reduce the data needed to learn prediction rules. The research demonstrates that forward-only access to quantum data does not offer an asymptotic query-complexity advantage over classical learning methods or learning from quantum data copies. The findings highlight the essential role of inverse access to state-preparation unitaries in achieving known improvements in realizable learning settings. AI

IMPACT Clarifies theoretical limitations and requirements for quantum advantage in machine learning data sampling.

RANK_REASON Academic paper detailing theoretical findings in quantum machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Quantum PAC learning sample complexity advantage requires inverse state-preparation access

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Academic paper detailing theoretical findings in quantum machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Natsuto Isogai, Satoshi Yoshida, Mio Murao ·

    Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries

    arXiv:2609.38403v1 Announce Type: cross Abstract: Whether quantum computation can reduce the amount of data sampled from an unknown probability distribution required to learn a prediction rule is a fundamental question in quantum machine learning. Quantum PAC learning studies thi…

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