Virtual Labs

Quantum Kernel Alignment in Machine Learning

What is the purpose of selecting a dataset at the beginning of the experiment?

a: To visualize the data distribution and inspect dataset properties before applying kernels Explanation

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b: To reduce the number of samples Explanation

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c: To automatically train the model Explanation

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d: To remove class labels Explanation

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Which classical kernels are available for comparison in the experiment?

a: Linear, Polynomial, and RBF Explanation

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b: Linear, Logistic, and Gaussian Explanation

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c: Decision Tree and Random Forest Explanation

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d: K-Means and DBSCAN Explanation

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What does the gamma parameter control when using the RBF kernel?

a: The learning rate of the model Explanation

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b: The influence range of individual data points Explanation

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c: The number of features in the dataset Explanation

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d: The number of classes in the dataset Explanation

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What is the main role of the quantum feature map in the experiment?

a: To compress the dataset Explanation

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b: To encode classical data into quantum states Explanation

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c: To remove irrelevant features Explanation

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d: To label the dataset automatically Explanation

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Which parameter determines how many quantum bits are used in the circuit?

a: Gamma Explanation

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b: Qubits Explanation

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c: Kernel type Explanation

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d: Dataset size Explanation

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What does the Kernel Alignment Score indicate in the experiment?

a: The training time of the model Explanation

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b: The similarity between classical and quantum kernel matrices Explanation

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c: The number of qubits used Explanation

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d: The number of samples in the dataset Explanation

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Which machine learning model is used for classification in this experiment?

a: Support Vector Machine (SVM) Explanation

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b: Decision Tree Explanation

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c: K-Nearest Neighbors Explanation

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d: Naive Bayes Explanation

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What is the purpose of comparing classical and quantum classification accuracy?

a: To determine which kernel representation performs better for the dataset Explanation

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b: To reduce the number of training samples Explanation

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c: To change the dataset labels Explanation

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d: To modify the quantum circuit automatically Explanation

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