Upload qads/quantum/qaoa.py

qads/quantum/qaoa.py

@@ -0,0 +1,170 @@
+"""QAOA (Quantum Approximate Optimization Algorithm) implementation."""
+import numpy as np
+from typing import Dict, Any, Optional, List, Tuple
+import time
+
+try:
+    import pennylane as qml
+    from pennylane import numpy as pnp
+    HAS_PENNYLANE = True
+except ImportError:
+    HAS_PENNYLANE = False
+
+
+class QAOAOptimizer:
+    """QAOA-based combinatorial optimization for path planning."""
+    
+    def __init__(self, config: Any):
+        self.config = config
+        self.n_qubits = config.n_qubits
+        self.n_layers = config.n_layers
+        self.shots = config.shots
+        self.max_iterations = config.max_iterations
+        self.learning_rate = config.learning_rate
+        
+        self.device = None
+        self._initialize_device()
+        
+    def _initialize_device(self):
+        """Set up quantum device."""
+        if HAS_PENNYLANE:
+            self.device = qml.device("default.qubit", wires=self.n_qubits, shots=self.shots)
+            
+    def optimize(self, 
+                cost_matrix: np.ndarray, 
+                constraints: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """Solve optimization problem using QAOA."""
+        start_time = time.time()
+        
+        if not HAS_PENNYLANE or self.device is None:
+            return self._simulated_optimization(cost_matrix, constraints, start_time)
+        
+        try:
+            result = self._pennylane_optimization(cost_matrix, constraints)
+            result['optimization_time'] = time.time() - start_time
+            result['backend'] = 'pennylane'
+            return result
+        except Exception as e:
+            return self._simulated_optimization(cost_matrix, constraints, start_time, str(e))
+    
+    def _pennylane_optimization(self, 
+                               cost_matrix: np.ndarray,
+                               constraints: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
+        """Core QAOA implementation using PennyLane."""
+        
+        @qml.qnode(self.device)
+        def qaoa_circuit(gamma, beta):
+            """QAOA circuit with cost and mixer Hamiltonians."""
+            # Initialize in superposition
+            for i in range(self.n_qubits):
+                qml.Hadamard(wires=i)
+            
+            # QAOA layers
+            for layer in range(self.n_layers):
+                # Cost Hamiltonian
+                for i in range(self.n_qubits):
+                    for j in range(i + 1, min(i + 4, self.n_qubits)):
+                        if i < cost_matrix.shape[0] and j < cost_matrix.shape[0]:
+                            weight = cost_matrix[i, j]
+                            qml.CNOT(wires=[i, j])
+                            qml.RZ(2 * gamma[layer] * weight, wires=j)
+                            qml.CNOT(wires=[i, j])
+                
+                # Single-qubit cost terms
+                for i in range(min(self.n_qubits, cost_matrix.shape[0])):
+                    qml.RZ(2 * gamma[layer] * cost_matrix[i, i], wires=i)
+                
+                # Mixer Hamiltonian
+                for i in range(self.n_qubits):
+                    qml.RX(2 * beta[layer], wires=i)
+            
+            return [qml.sample(qml.PauliZ(i)) for i in range(min(self.n_qubits, cost_matrix.shape[0]))]
+        
+        # Initialize parameters
+        gamma = pnp.random.uniform(0, np.pi, self.n_layers, requires_grad=True)
+        beta = pnp.random.uniform(0, np.pi, self.n_layers, requires_grad=True)
+        
+        # Cost function
+        def cost_fn(params):
+            g, b = params[:self.n_layers], params[self.n_layers:]
+            samples = qaoa_circuit(g, b)
+            # Compute expectation value as cost
+            n_nodes = min(self.n_qubits, cost_matrix.shape[0])
+            exp_cost = 0.0
+            for i in range(n_nodes):
+                for j in range(i+1, n_nodes):
+                    # Edge contribution based on measurement correlation
+                    exp_cost += cost_matrix[i, j] * (1 - np.mean(samples[i] * samples[j])) / 2
+            return exp_cost
+        
+        # Gradient descent
+        params = pnp.concatenate([gamma, beta])
+        opt = qml.GradientDescentOptimizer(stepsize=self.learning_rate)
+        
+        for i in range(self.max_iterations):
+            params = opt.step(cost_fn, params)
+            
+        # Extract solution
+        final_samples = qaoa_circuit(params[:self.n_layers], params[self.n_layers:])
+        binary_solution = [int(np.mean(s) > 0) for s in final_samples]
+        
+        path = [i for i, bit in enumerate(binary_solution[:cost_matrix.shape[0]]) if bit]
+        if not path:
+            path = list(range(min(self.n_qubits, cost_matrix.shape[0])))
+            
+        cost = sum(cost_matrix[path[i], path[i+1]] for i in range(len(path)-1))
+        
+        return {
+            'path': path,
+            'cost': float(cost),
+            'quantum_used': True,
+            'iterations': self.max_iterations,
+            'binary_solution': binary_solution,
+            'final_params': params.tolist()
+        }
+    
+    def _simulated_optimization(self,
+                               cost_matrix: np.ndarray,
+                               constraints: Optional[Dict[str, Any]] = None,
+                               start_time: float = None,
+                               error: str = "") -> Dict[str, Any]:
+        """Simulated QAOA behavior when quantum hardware is unavailable."""
+        import random
+        
+        n = cost_matrix.shape[0]
+        best_path = None
+        best_cost = float('inf')
+        
+        for iteration in range(self.max_iterations):
+            path = list(range(n))
+            random.shuffle(path)
+            
+            # Simulate quantum tunneling with random swaps
+            for _ in range(self.n_qubits):
+                i, j = random.sample(range(n), 2)
+                path[i], path[j] = path[j], path[i]
+            
+            cost = sum(cost_matrix[path[i], path[i+1]] for i in range(n-1))
+            
+            # Metropolis-like acceptance (simulated quantum amplitude)
+            if best_path is None or cost < best_cost:
+                best_path = path
+                best_cost = cost
+            elif random.random() < np.exp(-(cost - best_cost) / (1.0 + iteration)):
+                best_path = path
+                best_cost = cost
+        
+        if start_time is not None:
+            opt_time = time.time() - start_time
+        else:
+            opt_time = 0.0
+            
+        return {
+            'path': best_path,
+            'cost': float(best_cost),
+            'quantum_used': False,
+            'simulated': True,
+            'iterations': self.max_iterations,
+            'optimization_time': opt_time,
+            'error': error if error else None
+        }