An Innovative Genetic Algorithm for the Quantum Circuit Compilation Problem
Authors: Riccardo Rasconi, Angelo Oddi7707-7714
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our algorithm has been tested on a set of quantum circuit benchmark instances of increasing sizes available from the recent literature. We demonstrate that our genetic approach obtains very encouraging results that outperform the solutions obtained in previous research against the same benchmark, succeeding in significantly improving the makespan values for a great number of instances. |
| Researcher Affiliation | Academia | Riccardo Rasconi, Angelo Oddi Institute of Cognitive Sciences and Technologies, National Research Council of Italy (ISTC-CNR) Rome, Italy {riccardo.rasconi, angelo.oddi}@istc.cnr.it |
| Pseudocode | Yes | Algorithm 1 Genetic Algorithm; Algorithm 2 Decoding Procedure; Algorithm 3 Tournament Selection Procedure; Algorithm 4 Crossover Procedure (QCCP and QCCP-X); Algorithm 5 Mutation Operator (QCCP and QCCP-X) |
| Open Source Code | No | The text states: "the complete set of makespan values, together with the complete set of solutions are available at http://pst.istc.cnr.it/ angelo/qc/". This refers to results/solutions, not the source code for the methodology itself. |
| Open Datasets | Yes | Experimenting on a set of benchmark instances of different size belonging to the Quantum Approximate Optimization Algorithm (QAOA) class (Farhi, Goldstone, and Gutmann 2014; Guerreschi and Park 2017) tailored for the Max Cut problem and devised to be executed on top of a hardware architecture proposed by Rigetti Computing Inc. (Sete, Zeng, and Rigetti 2016)... We compare our approach against the QCCP benchmark originally proposed in (Venturelli et al. 2017), and subsequently expanded in (Booth et al. 2018) |
| Dataset Splits | No | The paper uses benchmark instances but does not specify any train/validation/test splits or cross-validation setup for these benchmarks. |
| Hardware Specification | Yes | all experiments have been performed on a 64-bit Windows10 O.S. running on Intel(R) Core(TM)2 Duo CPU E8600 @3.33 GHz with 8GB RAM, exactly as in (Oddi and Rasconi 2018). |
| Software Dependencies | No | The paper mentions "64-bit Windows10 O.S." but does not provide specific version numbers for any other software dependencies, libraries, or frameworks used in the experiments. |
| Experiment Setup | Yes | In the actual implementation of Algorithm 1, we have assigned the following values for the parameters: population Size = 50, tournmt Size = 5, xover Rate = 0.5, mutation Rate = 0.015, max V alue = 15, 30, 60 for benchmark size N = 8, 21, 40 respectively. |