Study of Feature Importance for Quantum Machine Learning Models

Baughman, Aaron K.; Yogaraj, Kavitha; Hebbar, Raja; Ghosh, S. K.; Haq, Rukhsan Ul; Chhabra, Yoshika

doi:10.48550/arxiv.2202.11204

Cited by 4 publications

(4 citation statements)

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“…Along this line, popular methods used in interpreting deep learning models include saliency maps and occlusion maps, which explore the importance of one pixel in an image to the final evaluation function. To our knowledge, there are few works focusing on this direction [111], and further explorations might be needed.…”

Section: Conclusion and Outlooksmentioning

confidence: 99%

Quantum Neural Network Classifiers: A Tutorial

Deng

2022

SciPost Phys. Lect. Notes

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Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.

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Section: Conclusion and Outlooksmentioning

confidence: 99%

Quantum Neural Network Classifiers: A Tutorial

Deng

2022

SciPost Phys. Lect. Notes

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“…We pass a solution to this function and it returns the fitness of that solution. In the below multi-objective fitness function (5), we aim to both maximize the accuracy and minimize the gate cost. A common strategy for multi-objective problem is to find the pareto front [1] by choosing high domination points with crowd distance techniques, however, pareto front method does not suit our situation because highaccuracy performance is usually hard to find.…”

Section: B Fitness and Matting Poolmentioning

confidence: 99%

“…Applying quantum mechanics on machine learning [4,21] is expected to have a performance on speeding up calculations. As for the classifier, quantum kernel method via support vector machine [10], among other development by different research groups [5,9,13,15,16,18,19,22], has been attested a powerful mean of using high dimensional quantum state space. These studies show the possibility of machine learning using quantum computers, which may have a boost in the future.…”

Section: Introductionmentioning

confidence: 99%

Effects of fitness function in genetically auto-generated quantum feature maps

Chen¹,

Chern²

2022

Preprint

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We present and probe some improvements over the method using genetic algorithm proposed in [S. Altares-López, Automatic design of quantum feature maps, (2021)] to automatically generate quantum feature maps for quantum-enhanced support vector machine, a classifier based on kernel method, by which we can access high dimensional Hilbert space efficiently. In addition, we define a multi-objective fitness function using penalty method, which incorporates maximizing the accuracy of classification and minimizing the gate cost of quantum feature map's circuit as the original method. Numerical results and comparisons with different kernel methods as well as the original proposed method are presented to demonstrate the efficiency of our fitness function. In particular, we reduce and optimize the gate cost of a circuit shown in [J.R. Glick, Covariant quantum kernels for data with group structure, (2022)] from 51 to 3 while remaining perfect accuracy.

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“…More recently, quantum computing has emerged as a promising platform for tackling computationally expensive combinatorial optimization tasks such as feature selection, offering innovative approaches to the challenges of dimensionality and data complexity [5][6][7][8]. This advancement complements the emergence of quantum machine learning, where quantum support vector machines (QSVM), demonstrate significant potential in leveraging quantum states for feature selection, transforming classical data into higher-dimensional Hilbert space for enhanced computational efficiency [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

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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Study of Feature Importance for Quantum Machine Learning Models

Cited by 4 publications

References 0 publications

Quantum Neural Network Classifiers: A Tutorial

Quantum Neural Network Classifiers: A Tutorial

Effects of fitness function in genetically auto-generated quantum feature maps

A novel feature selection method based on quantum support vector machine

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