McRy team submission

part1.py

@@ -0,0 +1,104 @@
+import qiskit as qk
+import matplotlib.pyplot as plt
+import numpy as np
+from collections import defaultdict
+
+from qiskit import QuantumCircuit, Aer, IBMQ
+from qiskit import transpile, assemble
+from qiskit.tools.jupyter import *
+from qiskit.visualization import plot_histogram
+from math import pi, ceil
+
+
+def bitfield(n):
+    return [1 if digit=='1' else 0 for digit in bin(n)[2:]]      
+
+def angle(img):
+    normal = np.max(img)
+    theta = (np.pi/2) / normal * img
+    return theta
+
+
+# accepts square matrix
+def encoder(img):
+    N = len(img)
+    thetas = angle(img)
+
+    qunum = ceil(np.log2(N*N)) + 1
+    qc = QuantumCircuit(qunum)
+
+    for i in range(qunum):
+        qc.h(i)
+
+    qc.barrier()
+
+    binary = prev_binary = 0
+
+    for i in range(N):
+        for j in range(N):
+            change = bitfield(binary^prev_binary)[:qunum]
+            for k, n in enumerate(change):
+                n and qc.x(k)
+
+            qc.mcry(thetas[i][j], [l for l in range(qunum - 1)], qunum - 1)
+
+            qc.barrier()
+            # increment to next pixel
+            prev_binary = binary
+            binary += 1
+
+    qc.measure_all()
+
+    return qc
+
+def simulate(qc):
+    aer_sim = Aer.get_backend('aer_simulator')
+    t_qc1 = transpile(qc, aer_sim)
+    qobj = assemble(t_qc1, shots=8192*256)
+    result = aer_sim.run(qobj).result()
+    counts = result.get_counts(qc)
+
+    return counts
+
+
+def decoder(histogram):
+    new_dict = defaultdict(lambda: np.zeros(2))
+
+    for key, value in histogram.items():
+        new_dict[key[1:]][int(key[0],2)] = value
+
+    N = 32
+    image = np.zeros([N, N])
+
+    for key, val in new_dict.items():
+        val/=sum(val)
+
+        a, b = divmod(int(key,2), N)
+
+        image[a][b] = np.arccos(np.sqrt(val[0]))
+
+    return image
+
+def run_part1(image):
+    N_orig = len(image)
+
+    img = np.zeros([32,32])
+
+    for i in range(N_orig):
+        for j in range(N_orig):
+            img[i][j] = image[i][j]
+
+    N = len(img)
+
+    plt.imshow(img[0:N,0:N])
+    plt.show()
+
+    qc = encoder(img)
+
+    histogram = simulate(qc)
+
+    image = decoder(histogram)
+
+    plt.imshow(image)
+    plt.show()
+    return image[:N_orig, :N_orig]