Quantum Algorithms for Non-smooth Non-convex Optimization

Authors: Chengchang Liu, Chaowen Guan, Jianhao He, John C. S. Lui

NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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.