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 Bayesian Optimization
Authors: Zhongxiang Dai, Gregory Kang Ruey Lau, Arun Verma, YAO SHU, Bryan Kian Hsiang Low, Patrick Jaillet
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use simulations, as well as an experiment using a real quantum computer, to verify that the theoretical quantum speedup achieved by our Q-GP-UCB is also potentially relevant in practice. |
| Researcher Affiliation | Academia | 1Department of Computer Science, National University of Singapore 2CNRS@CREATE, 1 Create Way, #08-01 Create Tower, Singapore 138602 3Department of Electrical Engineering and Computer Science, MIT |
| Pseudocode | Yes | Algorithm 1 Q-GP-UCB |
| Open Source Code | No | The paper states: "We use the Qiskit python package to implement the QMC algorithm (Lemma 1) following the recent work of [20]." and discusses running experiments on IBM quantum computers, but does not provide a link to their own source code for Q-GP-UCB. |
| Open Datasets | Yes | In the Auto ML experiment, an SVM is used for diabetes diagnosis. That is, we adopt the diabetes diagnosis dataset which can be found at https://www.kaggle.com/uciml/ pima-indians-diabetes-database, which is under the CC0 license. |
| Dataset Splits | Yes | We use 70% of the dataset as the training set and the remaining dataset as the validation set. |
| Hardware Specification | Yes | All our experiments are run on a computer with Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz, with 64 CPUs. |
| Software Dependencies | Yes | We use the Qiskit python package to implement the QMC algorithm (Lemma 1) following the recent work of [20]. |
| Experiment Setup | Yes | For our Q-GP-UCB, we choose beta_s = 1 + log s, s >= 1 following the order given by the theoretical value of beta_s (Theorem 3). For classical GP-UCB, we tried different approaches to setting beta_s: beta_s = sqrt(2) and beta_s = 1, all of which are commonly adopted practices in BO; we found that beta_s = sqrt(2) and beta_s = 1 have led to the best performances for GP-UCB in, respectively, the synthetic and Auto ML experiments. |