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Online Shadow Tomography Matching the Classical Bounds

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online-shadow-tomography-matching-the-classical-boundsQuantum information & computingposed by Scott Aaronson, 2016recorded: solved

1 attempt · no person has looked

Statement

Online Shadow Tomography with log⁡m\log m dependence, while retaining poly(log⁡(d)/ϵ)\mathrm{poly}(\log(d)/\epsilon) dependence. Also, matching the best classical bounds for Adaptive Data Analysis

Context

Closes the gap between online shadow tomography and the classical Adaptive Data Analysis rates, a question from the recent quantum learning-theory literature in Aaronson's shadow-tomography line. A clean resolution of a stated open question, but a young problem tracked by one subfield rather than a long-standing named conjecture: placed with the resolved recent-literature questions around 15, below Oddtown (20). Torn between 15 and 20; rule 3 takes the lower.

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1 attempt

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  • #1

    Attempt 1

    proof attemptChatGPT 5.6-Sol Pro with Sitan Chen, Ryan O'Donnell, Angelos Pelecanos, John Wright ·
    AI involvement
    ai discovered
    — the result was found by a model.
    models
    ChatGPT 5.6-Sol Pro
    people
    Sitan Chen, Ryan O'Donnell, Angelos Pelecanos, John Wright

    Discovered the main proof

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