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..
Non-asymptotic Approximation Error Bounds of Parameterized Quantum Circuits
Authors: Zhan Yu, Qiuhao Chen, Yuling Jiao, Yinan Li, Xiliang Lu, Xin Wang, Jerry Yang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further validate the approximation capability of PQCs through numerical experiments. |
| Researcher Affiliation | Academia | 1 School of Mathematics and Statistics, Wuhan University, Wuhan 430072, China 2 Centre for Quantum Technologies, National University of Singapore, 117543, Singapore 3 Hubei Key Laboratory of Computational Science, Wuhan 430072, China 4 Thrust of Artificial Intelligence, Information Hub, Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China |
| Pseudocode | No | The paper includes circuit diagrams (e.g., Figure 1) and detailed descriptions of algorithms and constructions in prose and mathematical notation but does not contain explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | Yes | We have provided the complete code in the supplementary materials that is necessary to reproduce the experimental results. |
| Open Datasets | No | We randomly sample 200 data points within the domain [0, 1] to create training and test datasets for D(x). |
| Dataset Splits | No | The paper mentions 'training and test datasets' but does not specify a separate validation split or explicit percentages/counts for training, validation, and test datasets. |
| Hardware Specification | Yes | Both learning processes are implemented on a Gold 6248 2.50 GHz Intel(R) Xeon(R) CPU. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not provide specific version numbers for software components or libraries. |
| Experiment Setup | Yes | Each parameter of the PQC is randomly initialized within the range [0, π]. We use the Adam optimizer [55] with a learning rate of 0.01 to minimize the Mean Squared Error (MSE) loss function during training. The training process was limited to a maximum of 300 iterations with a batch size of 100 data points. Early termination occurred if the MSE reached below 10−4. |