A Quantum/Classical Example Oracle Separation for Making Things Up
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Abstract (arXiv)
Consider two PAC learning algorithms, both having access to quantum computation, but differing in the types of examples they obtain: one is provided with classical samples, while the other is given quantum samples. Are there any learning tasks that can be efficiently performed by the latter, but not by the former? This question, the focus of our work, is surprisingly still open. Our main result is to show that relative to an oracle, there are distributions that can be efficiently generated by a quantum learner with access to quantum samples, but not by a quantum learner with access to only classical samples, making progress to answering this question in the affirmative.
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References · 10
- 2020A rigorous and robust quantum speed-up in supervised machine learningcited by 271
- 2020On the Quantum versus Classical Learnability of Discrete Distributionscited by 57
- 2016Optimal Quantum Sample Complexity of Learning Algorithmscited by 33
- 2023Exponential separations between classical and quantum learnerscited by 26
- 2023Improved machine learning algorithm for predicting ground state propertiescited by 22
- 2017Quantum query complexity of entropy estimationcited by 11
- 2019Distributional property testing in a quantum worldcited by 11
- 2016Breaking Symmetric Cryptosystems using Quantum Period Findingcited by 3
- 2025The Probably Approximately Correct Learning Model in Computational Learning Theorycited by 3
- 2025Conjugate queries can helpcited by 2
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