A Multiobjective Approach for Nearest Neighbor Optimization of N-Dimensional Quantum Circuits

Barán, Benjamín; Carballude, Alejo; Villagra, Marcos

doi:10.1007/s42979-020-00398-3

Cited by 2 publications

(2 citation statements)

References 39 publications

(78 reference statements)

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“…The selection operator is a multiobjetive Non-Sorted Genetic Algorithm II (NSGA-II). This algorithm decides which individuals survive to the next generation based on Pareto-dominance and density-based metrics [26]. This algorithm has a strong tendency to keep individuals with higher fitness, because it selects the individuals after ordering the population by dominance.…”

Section: Genetic Operatorsmentioning

confidence: 99%

Automatic design of quantum feature maps

Altares-López

Ribeiro

García-Ripoll

2021

Quantum Sci. Technol.

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We propose a new technique for the automatic generation of optimal ad-hoc ansätze 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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Section: Genetic Operatorsmentioning

confidence: 99%

Automatic design of quantum feature maps

Altares-López

Ribeiro

García-Ripoll

2021

Quantum Sci. Technol.

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“…On the other hand, genetic algorithms have been pivotal in feature selection [4,24], showing remarkable adaptability in quantum computing environments [25][26][27][28][29][30][31]. Auto generated quantum circuits using multiobjective genetic algorithms [32,33], in particular, the first integration of genetic algorithms in QSVM for automating feature map generation [25], address key challenges such as local minima and barren plateaus [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A novel feature selection method based on quantum support vector machine

Wang

2024

Phys. Scr.

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Feature selection is critical in machine learning to reduce dimensionality and improve model accuracy and efficiency. The exponential growth in feature space dimensionality for modern datasets directly results in ambiguous samples and redundant features, which can severely degrade classification accuracy. Quantum machine learning offers potential advantages for addressing this challenge. In this paper, we propose a novel method, quantum support vector machine feature selection (QSVMF), integrating quantum support vector machines with multi-objective genetic algorithm. QSVMF optimizes multiple simultaneous objectives: maximizing classification accuracy, minimizing selected features and quantum circuit costs, and reducing feature covariance. We apply QSVMF for feature selection on a breast cancer dataset, comparing the performance of QSVMF against classical approaches with the selected features. Experimental results show that QSVMF achieves superior performance. Furthermore, The Pareto front solutions of QSVMF enable analysis of accuracy versus feature set size trade-offs, identifying extremely sparse yet accurate feature subsets. We contextualize the biological relevance of the selected features in terms of known breast cancer biomarkers. This work highlights the potential of quantum-based feature selection to enhance machine learning efficiency and performance on complex real-world data.

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A Multiobjective Approach for Nearest Neighbor Optimization of N-Dimensional Quantum Circuits

Cited by 2 publications

References 39 publications

Automatic design of quantum feature maps

Automatic design of quantum feature maps

A novel feature selection method based on quantum support vector machine

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