Learning Distributions over Quantum Measurement Outcomes
Authors: Weiyuan Gong, Scott Aaronson
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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 <wygong8@gmail.com>. |
| 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. |