quantum_protein_development.py

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):
        @qml.qnode(self.dev)
        def quantum_circuit(inputs, params):
            for i in range(len(inputs)):
                wire_i = i % self.num_qubits
                next_wire = (i + 1) % self.num_qubits
                # Dendrite processing
                qml.RX(inputs[i], wires=wire_i)
                qml.RY(params[wire_i, 0], wires=wire_i)

                # Soma processing
                qml.RZ(params[wire_i, 1], wires=wire_i)
                qml.CNOT(wires=[wire_i, next_wire])

            return [qml.expval(qml.PauliZ(i % self.num_qubits)) for i in range(len(inputs))]

        return np.array(quantum_circuit(inputs, params))

    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)