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..
Purest Quantum State Identification
Authors: Yingqi Yu, Honglin Chen, Jun Wu, Wei Xie, Xiangyang Li
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically validate our theoretical results, we performed numerical simulations on classical simulators to evaluate the performance of the proposed method under finite-sample conditions. We considered a collection of 10 randomly generated 6-qubit quantum states... Each algorithm was run for 100 independent trials. The results are summarized as follows: IM_PQSI achieves a 53% success rate, with lower average purity 0.8736 and higher variance 0.001319. CM_PQSI achieves perfect success in all 100 runs and consistently selects the state with purity 0.9. Unadaptive achieves a 43% success rate, with lower average purity 0.86192 and higher variance 0.002145. |
| Researcher Affiliation | Academia | Yingqi Yu1 Honglin Chen1 Jun Wu1 Wei Xie1 Xiang-Yang Li1,2 1School of Computer Science and Technology, University of Science and Technology of China 2Hefei National Laboratory, University of Science and Technology of China EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Incoherent measurement based algorithm for solving PQSI problem (IM-PQSI) Algorithm 2 Coherent measurement based algorithm for solving PQSI problem (CM-PQSI) |
| Open Source Code | No | The paper does not explicitly provide a link to source code, a statement of code release, or mention code in supplementary materials for the described methodology. |
| Open Datasets | No | We considered a collection of 10 randomly generated 6-qubit quantum states of the form ρi = (1 λi) |ψi ψi| + λi Id where each |ψi is a pure state sampled uniformly at random, and the mixing parameter λi is chosen such that the purity Tr(ρ2 i ) = 0.5 + 0.04i. |
| Dataset Splits | No | The paper describes generating quantum states and running algorithms for 100 independent trials but does not specify explicit training, validation, or test dataset splits in the traditional sense. The 'N = 30,000 copies of the unknown quantum states' refers to the sample budget per trial. |
| Hardware Specification | No | To empirically validate our theoretical results, we performed numerical simulations on classical simulators to evaluate the performance of the proposed method under finite-sample conditions. (No specific hardware details are provided.) |
| Software Dependencies | No | The paper mentions 'classical simulators' for numerical simulations but does not specify any particular software, libraries, or their version numbers. |
| Experiment Setup | Yes | We considered a collection of 10 randomly generated 6-qubit quantum states of the form ρi = (1 λi) |ψi ψi| + λi Id where each |ψi is a pure state sampled uniformly at random, and the mixing parameter λi is chosen such that the purity Tr(ρ2 i ) = 0.5 + 0.04i. The task is to identify the state with the highest purity given N = 30,000 copies of the unknown quantum states. Each algorithm was run for 100 independent trials. |