Online Shadow Tomography Matching the Classical Bounds
Online Shadow Tomography with dependence, while retaining dependence. Also, matching the best classical bounds for Adaptive Data Analysis
- Result
- Proved
- Status
- Resolved
- AI contribution
- AI-discovered
- Method
- Argument
- Field
- Quantum information & computing
- Posed by
- Scott Aaronson
- Year posed
- 2016
- Years open
- 10y
- Solved
- 2026-07-31
- Model
- ChatGPT 5.6-Sol Pro
- Vendor
- OpenAI
- Collaborators
- Sitan Chen, Ryan O'Donnell, Angelos Pelecanos, John Wright
- Verification
- Unreviewed
- Publication
- Preprint
- Significance
- —
- Disclosed cost
- —
- Wikipedia
- No dedicated article
What the AI did
Discovered the main proof
Verification
Unreviewed preprint. The authors state they studied, refined and verified the model's ideas themselves and take full responsibility for every claim, proof and citation; that is the authors checking their own work, so it stays Unreviewed until someone independent looks. Recorded as Resolved rather than Partial because the stated target - matching the classical Adaptive Data Analysis rates - is fully achieved by Theorems 1.2 and 1.3. What remains open is whether those rates are optimal, which was never the question this entry records.
Source
- PaperarXiv
Submitted by WittyFerret553 on