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Experimental Results for Quantum (Enhanced) Support Vector Machine (QSVM) with Quantum Kernel Training (QKT)/Quantum Kernel Alignment (QKA) on NSL-KDD Dataset

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Preface

• In this page...

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Experimental Results

• The experimental results will be available soon...

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Local Optimizers

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ADAptive Moment (ADAM)

• The experimental results...

Note(s):

• For these results, it was used the Quantum Support Vector Classifier (QSVC) from Qiskit library, namely through the Qiskit Machine Learning module. For more information, see the following link:
• https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.QSVC.html
• Additionally, was also used the Quantum Kernel Trainer for the Quantum Kernel Training (QKT)/Quantum Kernel Alignment (QKA) complementar task, namely through the Qiskit Machine Learning module. For more information, see the following link:
• https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.kernels.algorithms.QuantumKernelTrainer.html
• For the optimization algorithm chosen for the Quantum Kernel Trainer, it was used the ADAptive Moment (ADAM) optimizer. For more information, see the following links:
• https://qiskit-community.github.io/qiskit-algorithms/stubs/qiskit_algorithms.optimizers.ADAM.html

Footnote(s):

• ¥ - This hyperparameter represents the Maximum Number of Iterations of the optimizer.
• § - This hyperparameter represents the Tolerance Error of the optimizer.
• † - This hyperparameter represents the Learning Rate of the optimizer.
• ‡ - The higher Accuracy values are highlighted in green and the lowest Accuracy values are highlighted in red.

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AccuMulated Squared GRADient (AMSGRAD)

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Conjugate Gradient (CG)

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Constrained Optimization By Linear Approximation (COBYLA)

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Limited-memory - Broyden-Fletcher-Goldfarb-Shanno - Bound (L-BFGS-B)

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Gaussian-Smoothed Line Search (GSLS)

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Gradient Descent (GD)

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Nakanishi-Fujii-Todo (NFT)

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Nelder Mead (NM)

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Parallelized - limited-memory - Broyden-Fletcher-Goldfarb-Shanno (P-BFGS)

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Powell

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Sequential Least SQuares Programming (SLSQP)

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Simultaneous Perturbation Stochastic Approximation (SPSA)

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Quantum Natural - Simultaneous Perturbation Stochastic Approximation (QNSPSA)

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Truncated Newton Conjugate (TNC)

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(Continuous) Univariate Marginal Distribution Algorithm (UMDA)

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Global Optimizers

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Controlled Random Search (CRS)

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DIviding RECTangles Locally-biased (DIRECT-L)

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DIviding RECTangles Locally-biased RANDndomized (DIRECT-L-RAND)

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Evolutionary Strategy-Carlos Henrique (ESCH)

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Improved Stochastic Ranking Evolution Strategy (ISRES)

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Note(s):

• For these results, was used the Quantum Support Vector Classifier (QSVC) from Qiskit library, namely through the Qiskit Machine Learning module. For more information, see the following link:
• https://qiskit-community.github.io/qiskit-machine-learning/stubs/qiskit_machine_learning.algorithms.QSVC.html

Footnote(s):

• TODO

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License

• MIT

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