Fix optimization process and update test cases for quantum protein de…

…velopment

quantum_protein_development.py

@@ -0,0 +1,105 @@
+import pennylane as qml
+import numpy as np
+
+class QuantumProteinFolding:
+    def __init__(self, num_qubits, num_layers):
+        self.num_qubits = num_qubits
+        self.num_layers = num_layers
+        self.dev = qml.device("default.qubit", wires=num_qubits)
+        self.params = np.random.uniform(low=-np.pi, high=np.pi, size=(num_layers, num_qubits, 3))
+
+@qml.qnode(device=qml.device("default.qubit", wires=1))
+def qubit_layer(params, input_val):
+    qml.RX(input_val, wires=0)
+    qml.RY(params[0], wires=0)
+    qml.RZ(params[1], wires=0)
+    return qml.expval(qml.PauliZ(0))
+
+class QuantumProteinFolding:
+    def __init__(self, num_qubits, num_layers):
+        self.num_qubits = num_qubits
+        self.num_layers = num_layers
+        self.dev = qml.device("default.qubit", wires=num_qubits)
+        self.params = qml.numpy.array(np.random.uniform(low=-np.pi, high=np.pi, size=(num_layers, num_qubits, 2)), requires_grad=True)
+
+    def quantum_protein_layer(self, inputs, params):
+        outputs = []
+        for i in range(0, len(inputs)):
+            qml.RX(inputs[i], wires=0)
+            outputs.append(qubit_layer(params=params[i % self.num_qubits], input_val=inputs[i]))
+        return np.array(outputs)
+
+    def forward(self, amino_acid_sequence):
+        x = np.array(amino_acid_sequence)
+        for layer in range(self.num_layers):
+            x = self.quantum_protein_layer(x, self.params[layer])
+        return x
+
+    def protein_folding_simulation(self, amino_acid_sequence):
+        """
+        Simulate protein folding using quantum circuits.
+
+        Args:
+        amino_acid_sequence (list): A list of numbers representing amino acids.
+
+        Returns:
+        np.array: Simulated protein structure.
+
+        Raises:
+        ValueError: If the amino_acid_sequence is empty.
+        """
+        if len(amino_acid_sequence) == 0:
+            raise ValueError("The amino acid sequence cannot be empty.")
+        return self.forward(amino_acid_sequence)
+
+    def optimize_folding(self, amino_acid_sequence, num_iterations=200):
+        """
+        Optimize the protein folding simulation.
+
+        Args:
+        amino_acid_sequence (list): A list of numbers representing amino acids.
+        num_iterations (int): Number of optimization iterations.
+
+        Returns:
+        np.array: Optimized protein structure.
+        """
+        opt = qml.AdamOptimizer(stepsize=0.05)
+
+        def cost(params):
+            self.params = params.reshape(self.num_layers, self.num_qubits, 2)
+            folded_protein = self.protein_folding_simulation(amino_acid_sequence)
+            # New cost function: minimize the sum of squares of the folded protein
+            return qml.math.sum(folded_protein**2)
+
+        initial_params = self.params.copy()
+        params = initial_params.flatten()
+
+        for i in range(num_iterations):
+            params, cost_val = opt.step_and_cost(cost, params)
+
+        self.params = params.reshape(self.num_layers, self.num_qubits, 2)
+        optimized_result = self.protein_folding_simulation(amino_acid_sequence)
+        initial_result = self.forward(amino_acid_sequence)
+
+        if qml.math.sum(optimized_result**2) < qml.math.sum(initial_result**2):
+            return optimized_result
+        else:
+            self.params = initial_params
+            return initial_result
+
+# Example usage
+if __name__ == "__main__":
+    num_qubits = 4
+    num_layers = 2
+    qpf = QuantumProteinFolding(num_qubits, num_layers)
+
+    # Example amino acid sequence (simplified as numbers)
+    amino_acid_sequence = [0.1, 0.2, 0.3, 0.4]
+
+    # Simulate protein folding
+    folded_protein = qpf.protein_folding_simulation(amino_acid_sequence)
+    print("Simulated folded protein structure:", folded_protein)
+
+    # Optimize folding
+    optimized_protein = qpf.optimize_folding(amino_acid_sequence)
+    print("Optimized folded protein structure:", optimized_protein)

tests/test_quantum_protein_development.py

@@ -0,0 +1,51 @@
+import unittest
+import numpy as np
+from quantum_protein_development import QuantumProteinFolding
+
+class TestQuantumProteinFolding(unittest.TestCase):
+    def setUp(self):
+        self.num_qubits = 4
+        self.num_layers = 2
+        self.qpf = QuantumProteinFolding(self.num_qubits, self.num_layers)
+
+    def test_initialization(self):
+        self.assertEqual(self.qpf.num_qubits, self.num_qubits)
+        self.assertEqual(self.qpf.num_layers, self.num_layers)
+        self.assertEqual(self.qpf.params.shape, (self.num_layers, self.num_qubits, 2))
+
+    def test_protein_folding_simulation(self):
+        amino_acid_sequence = [0.1, 0.2, 0.3, 0.4]
+        folded_protein = self.qpf.protein_folding_simulation(amino_acid_sequence)
+        self.assertIsInstance(folded_protein, np.ndarray)
+        self.assertEqual(len(folded_protein), 4)  # Expected output size matches input size
+
+    def test_optimize_folding(self):
+        amino_acid_sequence = [0.1, 0.2, 0.3, 0.4]
+        optimized_protein = self.qpf.optimize_folding(amino_acid_sequence, num_iterations=10)
+        self.assertIsInstance(optimized_protein, np.ndarray)
+        self.assertEqual(len(optimized_protein), 4)  # Expected output size matches input size
+
+    def test_empty_sequence(self):
+        with self.assertRaises(ValueError):
+            self.qpf.protein_folding_simulation([])
+
+    def test_large_sequence(self):
+        large_sequence = np.random.rand(100)
+        folded_protein = self.qpf.protein_folding_simulation(large_sequence)
+        self.assertEqual(len(folded_protein), 100)  # Expected output size matches input size
+
+    def test_optimization_improvement(self):
+        amino_acid_sequence = [0.1, 0.2, 0.3, 0.4]
+        initial_folding = self.qpf.protein_folding_simulation(amino_acid_sequence)
+        optimized_folding = self.qpf.optimize_folding(amino_acid_sequence, num_iterations=50)
+
+        # Calculate the cost (sum of squares) for both initial and optimized folding
+        initial_cost = np.sum(initial_folding**2)
+        optimized_cost = np.sum(optimized_folding**2)
+
+        # Check if the optimized cost is lower (better) than the initial cost
+        self.assertLess(optimized_cost, initial_cost,
+                        f"Optimization did not improve the folding. Initial cost: {initial_cost}, Optimized cost: {optimized_cost}")
+
+if __name__ == '__main__':
+    unittest.main()