Virtual Labs

Variational Quantum Eigen solver

What information must be entered when adding a new job in the scheduling interface?

a: Job Name, Duration, and Machine Explanation

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b: Dataset Name and Features Explanation

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c: Kernel Type and Gamma Explanation

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d: Energy Value and Iterations Explanation

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In the experiment, how is each job represented in the quantum model?

a: Each job is mapped to a dataset Explanation

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b: Each job is mapped to a classical bit Explanation

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c: Each job is mapped to a qubit Explanation

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d: Each job is mapped to a machine Explanation

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What is the purpose of defining ordering rules between jobs?

a: To reduce the number of machines Explanation

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b: To specify dependencies between jobs Explanation

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c: To increase the number of qubits Explanation

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d: To automatically train the optimizer Explanation

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How are scheduling constraints represented in the quantum optimization model?

a: As penalty terms in the Hamiltonian energy function Explanation

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b: As classical datasets Explanation

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c: As kernel matrices Explanation

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d: As training labels Explanation

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What happens to the Hamiltonian energy when a scheduling rule is violated?

a: The energy decreases Explanation

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b: The energy remains constant Explanation

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c: The energy increases Explanation

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d: The energy becomes zero Explanation

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What is the purpose of the ansatz circuit in this experiment?

a: To generate possible scheduling configurations using parameterized quantum gates Explanation

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b: To store job data Explanation

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c: To remove scheduling constraints Explanation

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d: To define the number of machines Explanation

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Which quantum gate is applied to each qubit in the ansatz circuit?

a: Hadamard gate Explanation

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b: Rx rotation gate Explanation

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c: Swap gate Explanation

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d: Toffoli gate Explanation

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What happens when the 'Run VQE Optimization' button is clicked?

a: Jobs are deleted from the schedule Explanation

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b: The classical optimizer begins adjusting circuit parameters to minimize energy Explanation

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c: The number of qubits is doubled Explanation

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d: All constraints are removed Explanation

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What information is displayed during the VQE optimization process?

a: Dataset labels Explanation

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b: Kernel matrices Explanation

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c: Parameter evolution and energy reduction over iterations Explanation

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d: Feature map selection Explanation

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What does the final optimized schedule represent after VQE completes?

a: The schedule with the highest energy Explanation

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b: A random scheduling configuration Explanation

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c: The schedule corresponding to the lowest Hamiltonian energy Explanation

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d: The schedule with the most rule violations Explanation

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