Temporal Planning for Compilation of Quantum Approximate Optimization Circuits
Authors: Davide Venturelli, Minh Do, Eleanor Rieffel, Jeremy Frank
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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 find 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 find n plans (with n = 10). |