Automatic design of quantum feature maps
We propose a new technique for the automatic generation of optimal ad-hoc anstze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ans...
| Autores: | , , |
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| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2021 |
| País: | España |
| Recursos: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/261972 |
| Acesso em linha: | http://hdl.handle.net/10261/261972 https://api.elsevier.com/content/abstract/scopus_id/85115203955 |
| Access Level: | acceso abierto |
| Palavra-chave: | Artificial intelligence Automatic quantum classifier generation Genetic algorithms Optimization Quantum computing Quantum machine learning |
| Resumo: | We propose a new technique for the automatic generation of optimal ad-hoc anstze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning. |
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