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..
Quantum speedup of non-linear Monte Carlo problems
Authors: Jose Blanchet, Yassine Hamoudi, Mario Szegedy, Guanyang Wang
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main algorithmic contribution of this paper is a quantum-inside-quantum MLMC algorithm to estimate nested expectations. Under standard technical assumptions, the algorithm achieves a cost of e O(1/ϵ) to produce an estimator with ϵ-accuracy, which is proven to be optimal among all quantum algorithms up to logarithmic factors. ... Detailed proofs are provided in the Appendix. ... Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? Answer: [NA] . Justification: It does not include experiments. |
| Researcher Affiliation | Academia | Jose Blanchet Stanford University EMAIL Yassine Hamoudi Université de Bordeaux, CNRS, La BRI EMAIL Mario Szegedy Rutgers University EMAIL Guanyang Wang Rutgers University EMAIL |
| Pseudocode | Yes | Algorithm 1 Classical MLMC for nested expectation: Al (l 1) ... Algorithm 2 Bl(x) when l 0 ... Algorithm 3 Al when l 1 ... Algorithm 4 Q-NESTEXPECT-0.8 |
| Open Source Code | No | Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material? Answer: [NA] . Justification: The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not mention using any specific datasets for empirical evaluation. It primarily focuses on theoretical algorithm design and analysis. The NeurIPS checklist also states: "Answer: [NA] . Justification: The paper does not include experiments." |
| Dataset Splits | No | The paper does not conduct experiments on specific datasets and therefore does not discuss dataset splits. The NeurIPS checklist indicates: "Answer: [NA] . Justification: The paper does not include experiments." |
| Hardware Specification | No | The paper is theoretical and does not include any experimental results that would require specific hardware. The NeurIPS checklist confirms: "Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [NA] . Justification: The paper does not include experiments." |
| Software Dependencies | No | The paper is theoretical and does not report any empirical results that would necessitate a list of software dependencies with version numbers. The NeurIPS checklist states: "Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] . Justification: The paper does not include experiments." |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs, rather than empirical experimentation. No hyperparameters, training configurations, or system-level settings are discussed. The NeurIPS checklist confirms this: "Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? Answer: [NA] . Justification: The paper does not include experiments." |