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..
Temporal Planning for Compilation of Quantum Approximate Optimization Circuits
Authors: Davide Venturelli, Minh Do, Eleanor Rieffel, Jeremy Frank
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report on experiments using several temporal planners to compile circuits of various sizes to a realistic hardware architecture. This early empirical evaluation suggests that temporal planning is a viable approach to quantum circuit compilation. |
| Researcher Affiliation | Collaboration | 1 Quantum Artificial Intelligence Laboratory, NASA Ames Research Center 2 Planning and Scheduling Group, NASA Ames Research Center 3 USRA Research Institute for Advanced Computer Science (RIACS) 4 Stinger Ghaffarian Technologies (SGT Inc.) |
| Pseudocode | No | Figure 3 shows a PDDL model snippet, which is a domain description language, not pseudocode or an algorithm block. |
| Open Source Code | Yes | The full set of PDDL model for all our tested problems is available at: https://ti.arc.nasa.gov/m/groups/asr/planning-and-scheduling/VentCirComp17_data.zip. |
| Open Datasets | Yes | To generate the graphs G for which a Max Cut needs to be found, for each grid size, we randomly generate 100 Erd os-R enyi graphs G [Erd os and R enyi, 1960]. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits. It describes problem generation and different problem classes but not data splitting for model training/evaluation. |
| Hardware Specification | Yes | The results were collected on a Red Hat Linux 2.4Ghz machine with 8GB RAM. |
| Software Dependencies | Yes | We select planners that performed well in the temporal planning track of previous IPCs, while at the same time representing a diverse set of planning technologies: (i) LPG: which is based on local search with restarts over action graphs [Gerevini et al., 2003]; (ii) Temporal Fast Downward (TFD): a heuristic forward state-space (FSS) search planner with post-processing to reduce makespan [Eyerich et al., 2009]; and (iii) SPGlan: partition the planning problem into subproblems that can be solved separately, while resolving the inconsistencies between partial plans using extended saddle-point condition [Wah and Chen, 2004; Chen and Wah, 2006]. We ran SGPlan (Ver 5.22) and TFD (Ver IPC2014) with their default parameters while for LPG (Ver TD 1.0) we ran all three available options: -speeed, -quality, and ο¬nd n plans (with n = 10). |
| Experiment Setup | Yes | The allocated cutoff time for different setting are as follow: (i) 10 minutes for N = 8, (ii) 30 minutes for P = 1, N = 21; (iii) 60 minutes for other cases. We ran SGPlan (Ver 5.22) and TFD (Ver IPC2014) with their default parameters while for LPG (Ver TD 1.0) we ran all three available options: -speeed, -quality, and ο¬nd n plans (with n = 10). |