TEST C1000-112 COLLECTION - VCE C1000-112 EXAM

Test C1000-112 Collection - Vce C1000-112 Exam

Test C1000-112 Collection - Vce C1000-112 Exam

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IBM C1000-112 Exam is an important certification for developers who want to work on quantum computing projects. It validates their knowledge and skills in quantum computing using Qiskit v0.2X and demonstrates their commitment to learning and professional development. Fundamentals of Quantum Computation Using Qiskit v0.2X Developer certification is recognized globally and can help candidates advance their careers in the field of quantum computing.

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The IBM C1000-112 exam covers a range of topics, including quantum mechanics, quantum circuits, quantum gates, and quantum algorithms. Candidates will be expected to demonstrate their understanding of the fundamental concepts of quantum computing, as well as their ability to develop and run quantum programs using Qiskit. C1000-112 Exam is intended for developers who have a basic understanding of computer science and programming, but no prior experience with quantum computing is required.

IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer Sample Questions (Q47-Q52):

NEW QUESTION # 47
To return the individual list of measurement outcomes for each individual shot (provided the backend supports it) which argument needs to be set to true in the execute function?

  • A. memory_shots
  • B. memory
  • C. single_shot
  • D. shots_status

Answer: B


NEW QUESTION # 48
Which Qiskit function is used to visualize the statevector of a quantum circuit?

  • A. plot_statevector()
  • B. visualize_state()
  • C. draw_statevector()
  • D. display_quantum_statevector()

Answer: A


NEW QUESTION # 49
In the quantum circuit, choose the best option to display the plot given below?
qc= QuantumCircuit(3)
qc.h(0)
qc.x(1)
qc.cx(0,1)
qc.h(2)
qc.cx(1,2)
qc.measure_all()
backend_qasm = BasicAer.get_backend('qasm_simulator')
job = execute(qc, backend_qasm,shots=1024)
result = job.result().get_counts()

  • A.
  • B.
  • C.
  • D.

Answer: A


NEW QUESTION # 50
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])
    qc.h(inp_reg[1])
    qc.x(ancilla[0])
    qc.draw()
  • C. qc.h(inp_reg[0:1])
    qc.x(ancilla[0])
    qc.draw()
  • D. qc.h(inp_reg)
    qc.x(ancilla)
    qc.draw()
  • E. qc.h(inp_reg[0:2])
    qc.x(ancilla[0])
    qc.draw()
  • F. qc.h(inp_reg) qc.h(inp_reg) qc.x(ancilla) qc.draw()

Answer: B,D,E


NEW QUESTION # 51
Predict the output of counts in the below-given snippet:
q = QuantumRegister(2,'q')
c = ClassicalRegister(2,'c')
qc = QuantumCircuit(q,c)
qc.h(0)
qc.h(1)
qc.measure([0,1],[0,1])
backend = BasicAer.get_backend('qasm_simulator')
job = execute(qc, backend, shots=100)
counts = job.result().get_counts()

  • A.
  • B.
  • C.
  • D.
  • E.

Answer: A


NEW QUESTION # 52
......

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