Interactive proofs for verifying (quantum) learning and testing

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Publicat a:arXiv.org (Oct 31, 2024), p. n/a
Autor principal: Caro, Matthias C
Altres autors: Eisert, Jens, Hinsche, Marcel, Ioannou, Marios, Nietner, Alexander, Ryan Sweke
Publicat:
Cornell University Library, arXiv.org
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022 |a 2331-8422 
035 |a 3123152664 
045 0 |b d20241031 
100 1 |a Caro, Matthias C 
245 1 |a Interactive proofs for verifying (quantum) learning and testing 
260 |b Cornell University Library, arXiv.org  |c Oct 31, 2024 
513 |a Working Paper 
520 3 |a We consider the problem of testing and learning from data in the presence of resource constraints, such as limited memory or weak data access, which place limitations on the efficiency and feasibility of testing or learning. In particular, we ask the following question: Could a resource-constrained learner/tester use interaction with a resource-unconstrained but untrusted party to solve a learning or testing problem more efficiently than they could without such an interaction? In this work, we answer this question both abstractly and for concrete problems, in two complementary ways: For a wide variety of scenarios, we prove that a resource-constrained learner cannot gain any advantage through classical interaction with an untrusted prover. As a special case, we show that for the vast majority of testing and learning problems in which quantum memory is a meaningful resource, a memory-constrained quantum algorithm cannot overcome its limitations via classical communication with a memory-unconstrained quantum prover. In contrast, when quantum communication is allowed, we construct a variety of interactive proof protocols, for specific learning and testing problems, which allow memory-constrained quantum verifiers to gain significant advantages through delegation to untrusted provers. These results highlight both the limitations and potential of delegating learning and testing problems to resource-rich but untrusted third parties. 
653 |a Questions 
653 |a Algorithms 
653 |a Quantum phenomena 
653 |a Machine learning 
653 |a Constraints 
700 1 |a Eisert, Jens 
700 1 |a Hinsche, Marcel 
700 1 |a Ioannou, Marios 
700 1 |a Nietner, Alexander 
700 1 |a Ryan Sweke 
773 0 |t arXiv.org  |g (Oct 31, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3123152664/abstract/embedded/IZYTEZ3DIR4FRXA2?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2410.23969