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Aim

Theory

Pretest

Procedure

Simulation

Posttest

References

Feedback

Quantum Support Vector Machines (QSVM)

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What is the primary goal of a standard Support Vector Machine (SVM)?

a: To cluster data points into similar groups. Explanation

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b: To find the optimal hyperplane that separates data points of different classes with the maximum margin. Explanation

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c: To reduce the dimensionality of the dataset. Explanation

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d: To predict continuous numerical values based on input features. Explanation

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How do classical SVMs typically handle non-linearly separable data?

a: By increasing the number of training epochs. Explanation

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b: By randomly dropping data points. Explanation

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c: By using the 'kernel trick' to map data into a higher-dimensional feature space. Explanation

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d: They cannot handle non-linearly separable data. Explanation

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In a Quantum Support Vector Machine (QSVM), what is the function of a Quantum Feature Map?

a: It classicalizes quantum data. Explanation

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b: It maps classical data vectors into a high-dimensional quantum Hilbert space. Explanation

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c: It reduces the number of qubits required. Explanation

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d: It directly outputs the final classification label. Explanation

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What is mathematically represented by the quantum kernel in QSVM?

a: The Euclidean distance between two classical points. Explanation

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b: The probability of measuring a zero state. Explanation

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c: The squared inner product (fidelity) between the quantum states representing two data points. Explanation

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d: The number of support vectors chosen. Explanation

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