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 Algorithms for Non-smooth Non-convex Optimization
Authors: Chengchang Liu, Chaowen Guan, Jianhao He, John C. S. Lui
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper considers the problem of finding the (δ,ǫ)-Goldstein stationary point of the Lipschitz continuous objective, which is a rich function class to cover a large number of important applications. We construct a novel zeroth-order quantum estimator for the gradient of the smoothed surrogate. Based on such estimator, we propose a novel quantum algorithm that achieves a query complexity of O(d3/2δ 1ǫ 3) on the stochastic function value oracle, where d is the dimension of the problem. We also improve the query complexity to O(d3/2δ 1ǫ 7/3) by introducing a variance reduction variant. We present these results in Section 3 and Appendix A. The Proof of Lemma 3.2. |
| Researcher Affiliation | Academia | Chengchang Liu* 1 Chaowen Guan* 2 Jianhao He# 1 John C.S. Lui 1 1 The Chinese University of Hong Kong 2 University of Cincinnati |
| Pseudocode | Yes | Algorithm 1 Quantum Gradient-Free Method (QGFM) and Algorithm 2 Fast Quantum Gradient-Free Method (QGFM+) |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for its proposed methods. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training or evaluation on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation on datasets. |
| Hardware Specification | No | The paper is theoretical and does not specify any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any software dependencies with specific version numbers for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not include details on experimental setup such as hyperparameters or training configurations. |