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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Fast Zeroth-Order Convex Optimization with Quantum Gradient Methods
Authors: Junhyung Lyle Kim, Brandon Augustino, Dylan Herman, Enrico Fontana, Jacob Watkins, Marco Pistoia, Shouvanik Chakrabarti
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 41,650 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,882 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 | This is a theoretical paper, and does not include experimental results. |
| Researcher Affiliation | Industry | Global Technology Applied Research, JPMorgan Chase New York, NY 10001 USA |
| Pseudocode | Yes | xt+1 = ΠX (xt η gxt) , gxt estimate Lemma 2.1 (QPSM) ... Φ(yt+1) = Φ(xt) η gxt, gxt estimate Lemma 2.1 and xt+1 arg min x X P DΦ(x, yt+1) (QMD) ... xt+1 = xt ηgxt, gxt estimate Theorem 4.1. (QGD) ... Φ( zt+1) = Φ(xt) ηgxt zt+1 arg min x X P DΦ(x, zt+1) and Φ( xt+1) = Φ(xt) ηgzt+1 xt+1 arg min x X P DΦ(x, xt+1), (QMP) |
| Open Source Code | No | This paper does not include experiments requiring code. |
| Open Datasets | No | This paper does not include experiments. |
| Dataset Splits | No | This paper does not include experiments. |
| Hardware Specification | No | This paper does not include experiments. |
| Software Dependencies | No | This paper does not include experiments. |
| Experiment Setup | No | This paper does not include experiments. |