Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Quantum algorithm for large-scale market equilibrium computation
Authors: Po-Wei Huang, Patrick Rebentrost
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,711 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,127 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical simulations of a system with 16384 buyers and goods support our theoretical results that our quantum algorithm provides a significant speedup. |
| Researcher Affiliation | Academia | 1Centre for Quantum Technologies, National University of Singapore, Singapore 117543 2Department of Computer Science, National University of Singapore, Singapore 117417 |
| Pseudocode | Yes | The full algorithm is shown in Algorithm 1, and the algorithmic guarantee is shown in Theorem 5. |
| Open Source Code | Yes | The codebase can be found at https://github.com/georgepwhuang/q-market-equilibrium. |
| Open Datasets | No | For our experiments, we generate the input data v where the value v is sampled from a uniform distribution with range (0, 1] and a normal distribution N(0.5, 0.25), where we resample values that fall outside the range of (0, 1]. The paper generates its own data and does not provide access to a publicly available dataset, nor does it refer to an established benchmark dataset. |
| Dataset Splits | No | The paper discusses algorithmic iterations and convergence but does not provide specific training, validation, or test dataset splits in terms of percentages or sample counts for data partitioning. |
| Hardware Specification | Yes | Our experiments are conducted on a single NVIDIA P100 GPU and written with the Py Torch library [99]. |
| Software Dependencies | No | Our experiments are conducted on a single NVIDIA P100 GPU and written with the Py Torch library [99]. While PyTorch is mentioned, a specific version number for it or any other software dependency is not provided in the text. |
| Experiment Setup | Yes | For QAE, we run for p T n = 512 iterations and set M O( p T n). We compensate for the loss in accuracy of the estimation by employing the median-ofmeans estimator [60], where we take the median of 3 estimators constructed from the mean of 7 samples from the QAE subroutine. For the PR dynamics, we iterate for T = 16 iterations. We rerun our quantum algorithm over 15 times. |