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On provable learning separations between classical and quantum learners| title | On provable learning separations between classical and quantum learners |
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| start_date | 2023/11/07 |
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| schedule | 11h |
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| online | no |
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| location_info | Salle 4052 |
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| summary | One of the key challenges of the quantum machine learning field is identifying learning problems where quantum learning algorithms can achieve a provable exponential advantage over classical learning algorithms. Proofs of separation for learning mostly work by reductions to plausible complexity theoretic assumptions of various types. Previously known examples of advantages are all contrived, and all rely on cryptographic methods to make learning hard for a classical learner. However, the broadly shared intuition is that learning separations should be most apparent when the learning task involves data from genuinely quantum sources (but still classical, i.e., measured out), which are very different from cryptographic problems. In this talk I will discuss this apparent discrepancy by providing a fine-grained analysis of existing learning separations and present new results showing how various types of advantages can be obtained, which will include advantages stemming from a large class of complex quantum physical systems. |
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| responsibles | Hamoudi |
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| submitted | published | | 2023/11/07 15:28 UTC |
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