On the Role of Entanglement and Statistics in Learning
Authors: Srinivasan Arunachalam, Vojtech Havlicek, Louis Schatzki
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we make progress in understanding the relationship between learning models when given access to entangled measurements, separable measurements and statistical measurements in the quantum statistical query (QSQ) model. To this end we prove the following results |
| Researcher Affiliation | Collaboration | Srinivasan Arunachalam IBM Quantum Almaden Research Center Srinivasan.Arunachalam@ibm.com Vojtˇech Havlíˇcek IBM Quantum T.J. Watson Research Center Vojtech.Havlicek@ibm.com Louis Schatzki Electrical and Computer Engineering University of Illinois, Urbana-Champaign louisms2@illinois.edu |
| Pseudocode | No | The paper describes theoretical concepts, models, and proof outlines but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code or provide links to a code repository. |
| Open Datasets | No | The paper describes theoretical models and concept classes but does not use or provide access information for any publicly available or open dataset for training purposes. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical concepts and proofs and does not mention any specific hardware used for computations or experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software details or version numbers for any libraries, solvers, or programming languages used. |
| Experiment Setup | No | The paper describes theoretical models and mathematical proofs, not practical experimental setups, hyperparameters, or training configurations. |