Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Learning of Quantum States
Authors: Scott Aaronson, Xinyi Chen, Elad Hazan, Satyen Kale, Ashwin Nayak
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,179 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,019 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give three different ways to prove our results using convex optimization, quantum postselection, and sequential fat-shattering dimension which have different advantages in terms of parameters and portability. |
| Researcher Affiliation | Collaboration | Scott Aaronson UT Austin EMAIL Xinyi Chen Google AI Princeton EMAIL Elad Hazan Princeton University and Google AI Princeton EMAIL Satyen Kale Google AI, New York EMAIL Ashwin Nayak University of Waterloo EMAIL |
| Pseudocode | Yes | Algorithm 1 RFTL for Quantum Tomography |
| Open Source Code | No | The paper does not mention or provide any links to open-source code for the described methodologies. |
| Open Datasets | No | The paper presents theoretical results and algorithms; it does not involve empirical training on publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper mentions that algorithms have 'run time exponential in the number of qubits in each iteration, but are entirely classical,' which refers to theoretical complexity, not specific hardware used for experiments. |
| Software Dependencies | No | The paper describes theoretical algorithms and proofs; it does not specify any software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and proofs rather than empirical experiments, and therefore does not include details on hyperparameters, training configurations, or other system-level settings for an experimental setup. |