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..
Online Learning of Pure States is as Hard as Mixed States
Authors: Maxime Meyer, Soumik Adhikary, Naixu Guo, Patrick Rebentrost
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove the surprising result that learning pure states in this setting is as hard as learning mixed states. More specifically, we show that both classes share almost the same sequential fat-shattering dimension, leading to identical regret scaling. We also generalize previous results on full quantum state tomography in the online setting to (i) the ̈-realizable setting and (ii) learning the density matrix only partially, using smoothed analysis. |
| Researcher Affiliation | Academia | Maxime Meyer Department of Mathematics & IPAL, IRL2955 National University of Singapore Singapore EMAIL Soumik Adhikary Centre for Quantum Technologies National University of Singapore Singapore EMAIL Naixu Guo Centre for Quantum Technologies National University of Singapore Singapore EMAIL Patrick Rebentrost Centre for Quantum Technologies & School of Computing National University of Singapore Singapore EMAIL |
| Pseudocode | No | The paper describes methodologies using mathematical formulations and proof strategies, but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks or figures. |
| Open Source Code | No | The paper does not contain any statements about releasing source code, nor does it provide links to any code repositories. The NeurIPS checklist also indicates "This paper does not include any experiment." |
| Open Datasets | No | The paper is purely theoretical and does not conduct experiments using datasets. No datasets are mentioned as being used or made publicly available. The NeurIPS checklist also states "This paper does not include any experiment." |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, therefore, no dataset splits are discussed. The NeurIPS checklist also states "This paper does not include any experiment." |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup that would require hardware specifications. The NeurIPS checklist also states "This paper does not include any experiment." |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies or specific version numbers for software used in experiments. The NeurIPS checklist also states "This paper does not include any experiment." |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. The NeurIPS checklist also states "This paper does not include any experiment." |