adding codes

Author: mhjensen

doc/src/week16/programs/qrbm4.py

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+import pennylane as qml
+import numpy as np
+# Number of visible and hidden qubits
+n_v, n_h = 2, 1
+dev = qml.device('default.qubit', wires=n_v+n_h)
+# Define a variational circuit (QNode)
+@qml.qnode(dev, interface='autograd')
+def circuit(params):
+# params is a vector of rotation angles
+# Prepare all qubits in |0>
+# Example ansatz: one layer of rotations + entangling gates
+    for i in range(n_v + n_h):
+        qml.RY(params[i], wires=i)
+# entangle visible to hidden
+        for i in range(n_v):
+            qml.CNOT(wires=[i, n_v])  # connect each visible i to hidden n_v
+    return qml.probs(wires=list(range(n_v)))
+
+# Example target distribution over 2 visible bits
+target = np.array([0.3, 0.2, 0.1, 0.4])  # must sum to 1
+def loss(params):
+    probs = circuit(params)  # model probabilities for visible states
+# Add small epsilon to avoid log(0)
+    return np.sum(target * np.log((target + 1e-9) / probs))
+def parameter_shift_grad(params):
+    grads = np.zeros_like(params)
+    shift = np.pi/2
+    for idx in range(len(params)):
+        shift_vector = np.zeros_like(params)
+        shift_vector[idx] = shift
+        probs_plus = circuit(params + shift_vector)
+        probs_minus = circuit(params - shift_vector)
+        loss_plus  = np.sum(target * np.log((target + 1e-9) / probs_plus))
+        loss_minus = np.sum(target * np.log((target + 1e-9) / probs_minus))
+        grads[idx] = 0.5 * (loss_plus - loss_minus)
+    return grads
+
+#Initialize parameters and perform a simple gradient descent
+params = np.random.normal(0, 0.1, size=(n_v+n_h,))
+learning_rate = 0.1
+for epoch in range(100):
+    grads = parameter_shift_grad(params)
+    params -= learning_rate * grads