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- Doctorate Programming Assignment Help
- Implementation of Quantum Fourier Transform (QFT) in Python
- How Our Experts Provide Superior Help with Doctorate Programming Assignments?
- Complete Set of Topics Addressed in Our Doctorate Programming Assignment Help Service
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Implementation of Quantum Fourier Transform (QFT) in Python
import numpy as np
from qiskit import QuantumCircuit, execute, Aer
def qft(circuit, n):
"""Apply Quantum Fourier Transform to the first n qubits of the given circuit."""
for qubit in range(n):
circuit.h(qubit)
for k in range(qubit + 1, n):
circuit.cp(np.pi / float(2 ** (k - qubit)), k, qubit)
for qubit in range(n // 2):
circuit.swap(qubit, n - qubit - 1)
# Example usage:
n = 3 # Number of qubits
qc = QuantumCircuit(n)
qft(qc, n)
qc.measure_all()
# Simulate the circuit
backend = Aer.get_backend('qasm_simulator')
result = execute(qc, backend, shots=1024).result()
counts = result.get_counts()
print(counts)
The Quantum Fourier Transform (QFT) is a crucial algorithm for quantum computing that transforms a quantum state into its frequency domain. This code snippet illustrates a quantum circuit implementing the QFT.
By implementing QFT, students gain hands-on experience with quantum gates, circuits, and operations, allowing them to explore and experiment with quantum algorithms in real-world scenarios.
The complexity of the QFT algorithm challenges students to develop and refine their problem-solving skills in quantum programming, which is valuable for tackling other advanced quantum computing problems.
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