Create quantum_optimization.py

quantum/algorithms/quantum_optimization.py

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+# quantum_optimization.py
+import numpy as np
+from qiskit import Aer
+from qiskit.circuit import QuantumCircuit
+from qiskit.algorithms import QAOA
+from qiskit.algorithms.optimizers import SLSQP
+from qiskit.primitives import Sampler
+from qiskit.quantum_info import Pauli
+from qiskit.utils import QuantumInstance
+
+class QuantumOptimizer:
+    def __init__(self, n_qubits, p):
+        """
+        Initialize the Quantum Optimizer using QAOA.
+
+        Parameters:
+        - n_qubits: Number of qubits for the optimization problem.
+        - p: Number of layers in the QAOA circuit.
+        """
+        self.n_qubits = n_qubits
+        self.p = p
+        self.optimizer = SLSQP(maxiter=100)
+        self.backend = Aer.get_backend('aer_simulator')
+        self.quantum_instance = QuantumInstance(self.backend)
+
+    def create_qaoa_circuit(self, gamma, beta):
+        """
+        Create a QAOA circuit for the Max-Cut problem.
+
+        Parameters:
+        - gamma: Rotation angles for the phase separation.
+        - beta: Rotation angles for the mixing.
+
+        Returns:
+        - QuantumCircuit: The constructed QAOA circuit.
+        """
+        circuit = QuantumCircuit(self.n_qubits)
+
+        # Apply Hadamard gates to initialize in superposition
+        circuit.h(range(self.n_qubits))
+
+        # Apply the QAOA layers
+        for layer in range(self.p):
+            # Phase separation
+            for qubit in range(self.n_qubits):
+                circuit.rz(2 * gamma[layer], qubit)
+            for qubit in range(self.n_qubits - 1):
+                circuit.cx(qubit, qubit + 1)
+                circuit.rz(2 * gamma[layer], qubit + 1)
+                circuit.cx(qubit, qubit + 1)
+
+            # Mixing
+            for qubit in range(self.n_qubits):
+                circuit.rx(2 * beta[layer], qubit)
+
+        return circuit
+
+    def objective_function(self, params):
+        """
+        Objective function to minimize.
+
+        Parameters:
+        - params: Parameters for the QAOA circuit (gamma and beta).
+
+        Returns:
+        - float: The objective value (energy).
+        """
+        # Split parameters into gamma and beta
+        gamma = params[:self.p]
+        beta = params[self.p:]
+
+        # Create the QAOA circuit
+        circuit = self.create_qaoa_circuit(gamma, beta)
+
+        # Measure the circuit
+        circuit.measure_all()
+
+        # Execute the circuit
+        sampler = Sampler(self.backend)
+        counts = sampler.run(circuit, shots=1024).result().get_counts()
+
+        # Calculate the objective value (energy)
+        objective_value = 0
+        for outcome, count in counts.items():
+            # Calculate the contribution of each outcome to the objective value
+            # Here, we assume a simple Max-Cut problem where we want to maximize the number of edges cut
+            # This part should be customized based on the specific problem being solved
+            objective_value += count * self.calculate_cut_value(outcome)
+
+        return -objective_value / 1024  # Minimize the negative objective value
+
+    def calculate_cut_value(self, outcome):
+        """
+        Calculate the cut value for a given outcome.
+
+        Parameters:
+        - outcome: The measurement outcome (string of bits).
+
+        Returns:
+        - int: The cut value for the given outcome.
+        """
+        # Example cut value calculation for a simple graph
+        # This should be customized based on the specific graph structure
+        cut_value = 0
+        # Implement logic to calculate cut value based on the outcome
+        return cut_value
+
+    def optimize(self):
+        """
+        Optimize the QAOA parameters to find the best solution.
+        
+        Returns:
+        - result: The optimization result containing the best parameters and objective value.
+        """
+        initial_params = np.random.rand(2 * self.p)  # Random initial parameters
+        result = self.optimizer.minimize(self.objective_function, initial_params)
+        return result
+
+if __name__ == "__main__":
+    # Example usage
+    n_qubits = 4  # Number of qubits for the optimization problem
+    p = 2         # Number of layers in the QAOA circuit
+
+    optimizer = QuantumOptimizer(n_qubits, p)
+    result = optimizer.optimize()
+
+    print(f"Optimal parameters: {result.x}")
+    print(f"Optimal objective value: {result.fun}")