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IBM C1000-112 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Use Qiskit Tools | 1% | - Monitor job status and retrieve results |
| Topic 2: Implement QASM | 1% | - Read/write and validate QASM files |
| Topic 3: Compare and Contrast Quantum Information | 10% | - Statevectors, unitaries, density matrices - Fidelity and quantum state analysis |
| Topic 4: Display and Use System Information | 3% | - Check Qiskit version and backend details |
| Topic 5: Implement BasicAer Simulators | 3% | - Use Python-based simulator backend |
| Topic 6: Perform Operations on Quantum Circuits | 47% | - Apply single-qubit and multi-qubit gates - Construct quantum registers and circuits - Add barriers and measure operations - Return circuit depth and OpenQASM strings |
| Topic 7: Executing Experiments | 3% | - Execute circuits on simulators and backends |
| Topic 8: Construct Visualizations | 19% | - Plot circuits, Bloch spheres, histograms |
| Topic 9: Return and Interpret Experiment Results | 7% | - Extract statevector and unitary data - Analyze histogram counts |
| Topic 10: Access Aer Provider | 6% | - Configure and use Aer simulators |
IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer Sample Questions:
What is the command used to retrieve the last run job from a backend?
- A. backend.jobs()[0]
- B. backend.jobs()[-1]
- C. backend.retrieve_job()[0]
- D. backend.retrieve_job()[1]
Correct Answer: A 🗳️
Which BasicAer simulator is commonly used to calculate the unitary matrix of a quantum circuit?
- A. ideal_simulator
- B. qasm_simulator
- C. noise_model_simulator
- D. unitary_simulator
Correct Answer: D 🗳️
In which scenarios would using noise_model_simulator in BasicAer be beneficial?
- A. When examining the ideal behavior of quantum circuits
- B. When evaluating the effects of noise on quantum computations
- C. When measuring qubits in a circuit
- D. When verifying the unitary matrix of a quantum gate
Correct Answer: B 🗳️
Given this code fragment, which output fits most closely with the measurement probability distribution?
qc = QuantumCircuit(2, 2)
qc.x(0)
qc.measure([0,1], [0,1])
simulator = Aer.get_backend('qasm_simulator')
result = execute(qc, simulator, shots=1000).result()
counts = result.get_counts(qc)
print(counts)
- A. {'01': 1000}
- B. {'00': 1000}
- C. {'10': 1000}
- D. {'11': 1000}
Correct Answer: A 🗳️
What property distinguishes a quantum computer from a classical computer?
- A. Larger memory capacity
- B. Utilization of quantum gates
- C. Ability to process complex arithmetic operations
- D. Quantum parallelism and superposition
Correct Answer: D 🗳️
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