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IBM C1000-112 Exam Syllabus Topics:

IBM Fundamentals of Quantum Computation Using Qiskit v0.2X Developer Sample Questions:

1. Which two code fragments, when inserted into the code below, will produce the statevector shown in the output?
from qiskit import QuantumCircuit, Aer, execute
from math import sqrt
qc = QuantumCircuit(2)
# Insert fragment here
simulator = Aer.get_backend('statevector_simulator')
result = execute(qc, simulator).result()
statevector = result.get_statevector()
print(statevector)
Output:
[0.707+0.j 0.+0.j 0.+0.j 0.707+0.j]

A) qc.h(0)
qc.cx(0,1)
B) qc.cx(0,1)
qc.measure_all()
C) v = [1/sqrt(2), 0, 0, 1/sqrt(2)]
qc.initialize(v,[0,1])
D) v1, v2 = [1,0], [0,1]
qc.initialize(v1,0)
qc.initialize(v2,1)
E) qc.h(0)
qc.h(1)
qc.measure_all()

2. Which Qiskit function is used to display the capabilities and parameters of a specific backend?

A) display_backend_properties()
B) inspect_backend()
C) get_backend_info()
D) view_backend_capabilities()

3. What is the primary advantage of quantum information processing over classical information processing?

A) Quantum information enables parallel processing and superposition
B) Quantum information is always error-free
C) Classical information is more secure than quantum information
D) Classical information can hold more data than quantum information

4. What Qiskit component enables simulation of quantum systems, including noise models and backends?

A) Qiskit Aer
B) Qiskit Aqua
C) Qiskit Ignis
D) Qiskit Terra

5. Which of the following commands will convert the below qasm string stored in the variable qasm_str to the quantum circuit?
OPENQASM 2.0;
include "qelib1.inc";
qreg q[3];
creg c[3];
h q[0];
cx q[0],q[1];
cx q[0],q[2];
barrier q[0],q[1],q[2];
measure q[0] -> c[0];
measure q[1] -> c[1];
measure q[2] -> c[2];

A) QuantumCircuit.qasm_str
B) QuantumCircuit.from_qasm_file(qasm_str)
C) QuantumCircuit.from_qasm_str()
D) QuantumCircuit.from_qasm_str(qasm_str)

Solutions: