Implementación Cuántica de un Algoritmo Genético

Mauricio Solar; Vicente Figueroa; Francisco Manriquez; Francisco Pizarro; Liuba Dombrovskaia

doi:10.36561/ING.27.14

Autores/as

Mauricio Solar Universidad Técnica Federico Santa María, Chile https://orcid.org/0000-0002-4433-4622
Vicente Figueroa Universidad Técnica Federico Santa María, Chile https://orcid.org/0009-0001-2391-8223
Francisco Manriquez Universidad Técnica Federico Santa María, Chile https://orcid.org/0009-0008-7045-4258
Francisco Pizarro Universidad Técnica Federico Santa María, Chile https://orcid.org/0009-0004-6317-6414
Liuba Dombrovskaia Universidad Técnica Federico Santa María, Chile https://orcid.org/0000-0001-6572-9765

DOI:

https://doi.org/10.36561/ING.27.14

Palabras clave:

Algoritmo Genético Cuántico, Computación Cuántica, Algoritmo Genético Cuántico Híbrido

Resumen

Este trabajo proporciona una visión generalizada del estado actual de los algoritmos genéticos cuánticos (QGAs), mostrando los avances realizados en esta área de investigación los últimos 24 años. Los QGAs combinan conceptos de la computación cuántica y los algoritmos genéticos clásicos (CGAs), lo que les permite abordar problemas complejos de optimización y búsqueda de manera eficiente. Se presentan los principales hallazgos y contribuciones de estos algoritmos cuánticos destacando las tendencias y los enfoques más prometedores, así como los desafíos y limitaciones que deben superarse. Se presentan nuevos enfoques y técnicas de implementación de QGAs, incluyendo operadores genéticos cuánticos y esquemas de codificación eficientes que contribuyen a mejorar el rendimiento y la convergencia de los algoritmos. Se comparan los QGAs y otros enfoques similares, como los CGAs y los algoritmos cuánticos puros, destacando las ventajas y desventajas relativas de los QGAs en comparación a sus versiones clásicas. Se muestra también una implementación de QGA utilizando la biblioteca Qiskit. Se presentan la selección de los métodos usados para la generación de la población inicial, el cruzamiento y la mutación de las distintas poblaciones de los circuitos cuánticos simulados en los experimentos realizados, ejemplificando las ventajas significativas que estos pueden traer en comparación con los enfoques clásicos.

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Citas

Lee Spector, Howard Barnum, Herbert J. Bernstein, Nikhil Swamy. “7: Quantum Computing Applications of Genetic Programming”. The MIT Press. 1999. https://doi.org/10.7551/mitpress/1110.003.0010

Mohammad Mojrian y Seyed Abolghasem Mirroshandel. “A novel extractive multi-document text summarization system using quantum-inspired genetic algorithm: MTSQIGA”. Expert Systems with Applications 171 (2021), pp. 114555. issn: 0957-4174. doi: https://doi.org/10.1016/j.eswa.2020. 114555. url: https://www.sciencedirect.com/science/article/pii/S0957417420311994.

Kuk-Hyun Han y Jong-Hwan Kim. “Genetic quantum algorithm and its application to combinatorial optimization problem”. Proc. of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512). 2 (2000), pp. 1354-1360, vol. 2. doi: 10.1109/CEC.2000.870809.

Andrea Malossini, Enrico Blanzieri y Tommaso Calarco. “Quantum Genetic Optimization”. IEEE Transactions on Evolutionary Computation 12.2 (2008), pp. 231-241. doi: 10.1109/TEVC.2007.905006.

Y. Hardy y W.-H Steeb. “Genetic Algorithms and Optimization Problems in Quantum

Computing”. Int. Journal of Modern Physics C -IJMPC 21 (2010), pp. 1359-1375. doi: 10.1142/S0129183110015890.

Huaixiao Wang et al. “The Improvement of Quantum Genetic Algorithm and Its Application on Function Optimization”. Mathematical Problems in Engineering (2013). doi: 10.1155/2013/730749.

Hatem M. H. Saad et al. “Quantum-Inspired Genetic Algorithm for Resource-

Constrained Project-Scheduling”. IEEE Access 9 (2021), pp. 38488-38502. doi: 10.1109/ACCESS.2021.3062790.

Enrique Ballinas y Oscar Montiel Ross. “Hybrid Quantum Genetic Algorithm for the 0-1 Knapsack Problem in the IBM Qiskit Simulator”. Computacion y Sistemas 26 (2022), pp. 725-742. doi: 10.13053/CyS-26-2-4253.

Arufe L, González MA, Oddi A, Rasconi R, Varela R. “Quantum circuit compilation by genetic algorithm for quantum approximate optimization algorithm applied to MaxCut problem”. Swarm Evol Comput 69:101030. (2022) https://doi.org/10.1016/j.swevo.2022.101030

In‘es Hilali-Jaghdam et al. “Quantum and classical genetic algorithms for multilevel segmentation of medical images: A comparative study”. Computer Communications 162 (2020), pp. 83-93. doi: https://doi.org/10.1016/j.comcom.2020.08.010. url: https://www.sciencedirect.com/science/article/pii/S0140366420318892.

Lensen A, Xue B, Zhang M (2021). “Genetic programming for evolving a front of interpretable models for data visualization”. IEEE Trans Cybern 51(11):5468–5482. https://doi.org/10.1109/TCYB.2020.2970198

Zhaoyang Huang et al. “Optimal design of load frequency active disturbance rejection control via double-chains quantum genetic algorithm”. Neural Computing and Applications 33 (2021). doi: 10.1007/s00521-020-05199-6.

Durán, C., Carrasco, R., Soto, I. et al. “Quantum algorithms: applications, criteria and metrics. Complex”. Intell. Syst. 9, 6373–6392 (2023). https://doi.org/10.1007/s40747-023-01073-9

Qian X, Wang S, Li C, Wang G (2019). “Multi channels data fusion algorithm on quantum genetic algorithm for sealed relays”. J Phys Conf Ser 1237(2):22130. https://doi.org/10.1088/17426596/1237/2/022130

Xinjian Pan et al. “Self-calibration for linear structured light 3D measurement system based on quantum genetic algorithm and feature matching”. Optik 225

(2021), pp. 165749. issn: 0030-4026. doi: https://doi.org/10.1016/j.ijleo.2020.165749. url: https://www.sciencedirect.com/science/article/pii/S003040262031576X.

Yuxing Wang y Chunyu Wei. “Design optimization of office building envelope based on quantum genetic algorithm for energy conservation”. Journal of Building Engineering 35 (2021), pp. 102048. issn: 2352-7102. doi:https://doi.org/10.1016/j.jobe.2020.102048. url: https://www.sciencedirect.com/science/article/pii/S2352710220336809.

Jia-Chu Lee et al. “Quantum genetic algorithm for dynamic economic dispatch with valve-point effects and including wind power system”. Int. Journal of Electrical Power Energy Systems 33 (2011), pp. 189-197. doi: 10.1016/j.ijepes.2010.08.014.

Wang B, Zhao W, Lin S, Ke J, Wu H (2022). “Integrated energy management of highway service area based on improved multiobjective quantum genetic algorithm”. Dianwang Jishu/Power Syst Technol 46(5):1742–1751. https://doi.org/10.13335/j.1000-3673.pst.2021.1610

Junhe Wan et al. “Fractional-Order PID Motion Control for AUV Using Cloud-Model-Based Quantum Genetic Algorithm”. IEEE Access 7 (2019), pp. 124828-124843. doi: 10.1109/ACCESS.2019.2937978.

Zhu X, Xiong J, Liang Q (2018). “Fault diagnosis of rotation machinery based on support vector machine optimized by quantum genetic algorithm”. IEEE Access 6:33583–33588. https://doi.org/10.1109/ACCESS.2018.2789933

Guangfeng Cheng, Chunhua Wang y Cong Xu. “A novel hyper-chaotic image encryption scheme based on quantum genetic algorithm and compressive sensing”. Multimedia Tools and Applications 79 (2020). doi: 10.1007/s11042-020-09542-w.

Susan Stepney y John A Clark. “Evolving quantum programs and protocols”. Handbook of Theoretical and Computational Nanotechnology 3 (2006), pp. 113-160.

R. Lahoz-Beltra. “Quantum Genetic Algorithms for Computer Scientists”. Computers 5 (2016), pp. 24. doi: 10.3390/computers5040024.

Mahboobeh Houshmand et al. “An Evolutionary Approach to Optimizing Teleportation Cost in Distributed Quantum Computation”. Int. Journal of Theoretical Physics 59 (2020). doi: 10.1007/s10773-020-04409-0.

Rui Li et al. “Approximate Quantum Adders with Genetic Algorithms: An IBM Quantum Experience”. Quantum Measurements and Quantum Metrology 4 (2017), pp. 1. doi: 10.48550/arXiv.1611.07851.

B. I. P. Rubinstein, .Evolving quantum circuits using genetic programming,"Proc. of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), Seoul, Korea (South), 2001, pp. 144-151 vol. 1, doi: 10.1109/CEC.2001.934383.

Riccardo Rasconi y Angelo Oddi. “An Innovative Genetic Algorithm for the Quantum Circuit Compilation Problem”. Proc. of the AAAI Conf. on Artificial Intelligence 33 (2019), pp. 77077714. doi: 10.1609/aaai.v33i01.33017707.

Zakaria Laboudi y Salim Chikhi. “Comparison of Genetic Algorithm and Quantum Genetic Algorithm”. Int. Arab Journal of Information Technology 9, (2012).

Akira SaiToh, Robabeh Rahimi y Mikio Nakahara. “A quantum genetic algorithm with quantum crossover and mutation operations”. Quantum Information Processing 13 (2014), pp. 737. doi: 10.1007/s11128-013-0686-6.

Jirayu Supasil, Poramet Pathumsoot y Sujin Suwanna. “Simulation of implementable quantumassisted genetic algorithm”. Journal of Physics: Conference Series 1719 (2021), pp. 012102. doi:

1088/1742-6596/1719/1/012102.

Bart Rylander, Terence Soule, James A. Foster, Jim Alves-Foss. “Quantum Genetic Algorithms.”

Proc. of the Genetic and Evolutionary Computation Conference (GECCO ’00), Las Vegas, Nevada, USA, July 8-12, 2000

James King, Masoud Mohseni, William Bernoudy, Alexandre Fréchette, Hossein Sadeghi, Sergei V. Isakov, Hartmut Neven, Mohammad H. Amin. “Quantum-Assisted Genetic Algorithm”. arXiv:1907.00707, 2019.

Ruben Ibarrondo, Giancarlo Gatti, Mikel Sanz. “Quantum vs classical genetic algorithms: A numerical comparison shows faster convergence”. 2022 IEEE Symposium Series on Computational Intelligence (SSCI). http://dx.doi.org/10.1109/SSCI51031.2022.10022159, 10.1109/ssci51031.2022.10022159

Yu-Fang C, Hao X, Wen-Cong H, Liang Z (2018). “An improved multi-objective quantum genetic algorithm based on cellular automaton”. In: 2018 IEEE 9th Int. Conf. on Software Engineering and Service Sciences. IEEE, Beijing, pp 342–345. https://doi.org/10.1109/ICSESS.2018.8663840

Creevey, F.M., Hill, C.D. Hollenberg, L.C.L. “GASP: a genetic algorithm for state preparation on quantum computers”. Sci Rep 13, 11956 (2023). https://doi.org/10.1038/s41598-023-37767-w