Exam Name: Fundamentals of Quantum Computation Using Qiskit v0.2X Developer
Certification Provider: IBM
Corresponding Certification: IBM Certified Associate Developer
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IBM C1000-112 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Noise and Real Hardware | - IBM Quantum hardware execution - Quantum noise and error sources |
| Qiskit Programming | - Simulators and Aer backend usage - Building and executing circuits - Qiskit Terra basics |
| Quantum Gates and Circuits | - Multi-qubit gates (CNOT, controlled operations) - Quantum circuit construction - Single-qubit gates (X, Y, Z, H) |
| Quantum Computing Fundamentals | - Superposition and entanglement - Qubits and quantum states - Measurement and collapse |
| Quantum Algorithms | - Basic algorithmic complexity concepts - Deutsch-Jozsa algorithm - Grover's search algorithm |
Your IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer Exam Questions Answered
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IBM structures the IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer exam around 5 domains. The heaviest hitters are Quantum Algorithms, Qiskit Programming, Quantum Gates and Circuits. Start your revision where the weight is, and scroll up to the outline section on this page for the complete topic list.
The C1000-112 exam is the official IBM assessment that awards the IBM Certified Associate Developer - Quantum Computation using Qiskit v0.2X certification — a credential at the Associate level. Passing it proves vendor-validated skills that employers actively look for. It belongs to a broader track that includes IBM Quantum Developer, IBM Certified Associate Developer - Quantum Computation using Qiskit, making it a solid anchor for further certifications.
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IBM points IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer candidates toward these official training resources:
Coursework builds understanding; the 250 practice questions for the C1000-112 exam from ExamPrepAway then prove whether that understanding survives a real testing clock.
Basic understanding of linear algebra and Python programming recommended Because IBM can revise these requirements, verify the current rules on the official exam page — https://www.ibm.com/training/certification — before scheduling your IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer exam.
IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer Sample Questions:
Predict the output of the average_gate_fidelity, process_fidelity of the below snippet:
a = Operator(XGate())
b = np.exp(1j /3) * a
average_gate_fidelity(a,b)
process_fiedlity(a,b)
- A. average_gate_fidelity will be 0 and process_fidelity will be 0.5
- B. average_gate_fidelity will be 1.0 and process_fidelity will be 0.5
- C. average_gate_fidelity will be 0.5 and process_fidelity will be 1.0
- D. average_gate_fidelity will be 1.0 and process_fidelity will be 1.0
Correct Answer: D 🗳️
Which one of the below statements is invalid when drawing the quantum circuit?
- A. qc.draw(output='text')
- B. qc.draw(output='mpl')
- C. qc.draw(output='latex')
- D. qc.draw(output='png')
Correct Answer: D 🗳️
Assuming the fragment below, which three code fragments would produce the circuit illustrated?
inp_reg = QuantumRegister(2, name='inp')
ancilla = QuantumRegister(1, name='anc')
qc = QuantumCircuit(inp_reg, ancilla)
# Insert code here
- A. qc.h(inp_reg[1])
qc.h(inp_reg[2])
qc.x(ancilla[1])
qc.draw() - B. qc.h(inp_reg[0:2])
qc.x(ancilla[0])
qc.draw() - C. qc.h(inp_reg[0])
qc.h(inp_reg[1])
qc.x(ancilla[0])
qc.draw() - D. qc.h(inp_reg) qc.h(inp_reg) qc.x(ancilla) qc.draw()
- E. qc.h(inp_reg[0:1])
qc.x(ancilla[0])
qc.draw() - F. qc.h(inp_reg)
qc.x(ancilla)
qc.draw()
Correct Answer: B,C,F 🗳️
Given the following code, what is the depth of the circuit?
qc = QuantumCircuit(2, 2)
qc.h(0)
qc.barrier(0)
qc.cx(0,1)
qc.barrier([0,1])
- A. 4
- B. 5
- C. 3
- D. 2
Correct Answer: D 🗳️
Which of the following is the best option to plot the |+> basis in plot_bloch_vector?
- A. plot_bloch_vector([0,1,0])
- B. plot_bloch_vector([0,0.5,0])
- C. plot_bloch_vector([0,0,1])
- D. plot_bloch_vector([1,0,0])
Correct Answer: D 🗳️
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