Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Distributions over Quantum Measurement Outcomes
Authors: Weiyuan Gong, Scott Aaronson
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here, we propose an online shadow tomography procedure that solves this problem with high success probability requiring O(K log2 M log d/ϵ4) copies of ρ. We further prove an information-theoretic lower bound showing that at least Ω(min{d2, K +log M}/ϵ2) copies of ρ are required to solve this problem with high success probability. |
| Researcher Affiliation | Academia | 1IIIS, Tsinghua University 2Department of Computer Science, University of Texas at Austin. Correspondence to: Weiyuan Gong <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 RFTL for Quantum Tomography of K-outcome POVMs |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on real-world datasets, thus there is no mention of dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data, therefore, no training/test/validation dataset splits are discussed. |
| Hardware Specification | No | The paper is a theoretical work and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper focuses on theoretical algorithms and proofs, and does not list any specific software dependencies with version numbers required for reproduction. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments or their setup, including hyperparameters or training configurations. |