Stochastic Quantum Sampling for Non-Logconcave Distributions and Estimating Partition Functions
Authors: Guneykan Ozgul, Xiantao Li, Mehrdad Mahdavi, Chunhao Wang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present quantum algorithms for sampling from possibly non-logconcave probability distributions expressed as π(x) exp( βf(x)) as well as quantum algorithms for estimating the partition function for such distributions. Our quantum algorithms exhibit polynomial speedups in terms of dimension or precision dependencies when compared to best-known classical algorithms under similar assumptions. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Engineering, Pennsylvania State University 2Department of Mathematics, Pennsylvania State University. |
| Pseudocode | Yes | Algorithm 1 Unadjusted Langevin Algorithm (ULA) and Algorithm 2 Metropolis Adjusted Langevin Algorithm (MALA) are provided in Appendix B.3 and B.4 respectively. |
| Open Source Code | No | The paper does not contain an explicit statement about the release of source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets; therefore, no information regarding dataset availability or training data is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with datasets; therefore, no information regarding training/test/validation splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. No such details are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |