Create shors_algorithm.py

quantum_integration/quantum_algorithms/shors_algorithm.py

@@ -0,0 +1,52 @@
+from qiskit import QuantumCircuit, Aer, transpile, assemble, execute
+import numpy as np
+from qiskit.visualization import plot_histogram
+
+def shors_algorithm(N):
+    # Step 1: Choose a random a
+    a = np.random.randint(2, N)
+
+    # Step 2: Create the quantum circuit
+    n_count = 2 * N.bit_length()  # Number of counting qubits
+    qc = QuantumCircuit(n_count, n_count)
+
+    # Step 3: Apply the quantum Fourier transform
+    for i in range(n_count):
+        qc.h(i)  # Initialize counting qubits
+
+    # Step 4: Apply controlled-U operations
+    for i in range(n_count):
+        qc.append(controlled_U(a, N, 2**i), [i] + list(range(n_count)))
+
+    # Step 5: Apply the inverse QFT
+    qc = inverse_qft(qc, n_count)
+
+    # Step 6: Measure the result
+    qc.measure(range(n_count), range(n_count))
+
+    # Execute the circuit
+    backend = Aer.get_backend('qasm_simulator')
+    result = execute(qc, backend, shots=1024).result()
+    counts = result.get_counts()
+
+    # Plot the results
+    plot_histogram(counts).show()
+
+    return counts
+
+def controlled_U(a, N, power):
+    # Create a controlled-U operation
+    qc = QuantumCircuit(N.bit_length())
+    # Implement the modular exponentiation U|x> = |(a^x mod N)>
+    # (This is a placeholder; actual implementation will depend on N and a)
+    return qc
+
+def inverse_qft(qc, n):
+    # Implement the inverse Quantum Fourier Transform
+    for i in range(n):
+        qc.h(i)
+        for j in range(i):
+            qc.cp(-np.pi / (2 ** (i - j)), j, i)
+    for i in range(n // 2):
+        qc.swap(i, n - i - 1)
+    return qc