Update app.py

app.py

@@ -1,460 +1,378 @@
-import gradio as gr
 import numpy as np
 import torch
-import json
-import matplotlib.pyplot as plt
-import plotly.graph_objects as go
-import plotly.express as px
-from typing import Dict, List, Tuple, Any
+import torch.nn as nn
+import torch.optim as optim
+from typing import Dict, List, Tuple, Any, Optional
 import logging
+from dataclasses import dataclass
+from scipy.optimize import minimize
+import json
 
-# Import your quantum AI agent (assuming it's in quantum_agent.py)
-from quantum_agent import QuantumAIAgent, QuantumState, QuantumCircuit
-
-# Configure logging
-logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-class QuantumAIInterface:
-    """Gradio interface for the Quantum AI Agent."""
+@dataclass
+class QuantumState:
+    """Represents a quantum state."""
+    amplitudes: np.ndarray
+    num_qubits: int
+    fidelity: float = 1.0
     
-    def __init__(self):
-        self.agent = QuantumAIAgent()
-        logger.info("Quantum AI Interface initialized")
+    def __post_init__(self):
+        """Normalize amplitudes after initialization."""
+        self.amplitudes = self.amplitudes / np.linalg.norm(self.amplitudes)
+
+@dataclass
+class QuantumCircuit:
+    """Represents a quantum circuit."""
+    gates: List[str]
+    parameters: np.ndarray
+    num_qubits: int
+    depth: int
     
-    def optimize_vqe(self, num_qubits: int, optimization_steps: int) -> Tuple[str, str]:
-        """VQE optimization interface."""
-        try:
-            # Create a random Hamiltonian
-            dim = 2**num_qubits
-            hamiltonian = np.random.random((dim, dim))
-            hamiltonian = hamiltonian + hamiltonian.T  # Make Hermitian
-            
-            # Initialize random parameters
-            initial_params = np.random.random(num_qubits * 2)
-            
-            # Run optimization
-            result = self.agent.optimize_quantum_algorithm("VQE", hamiltonian, initial_params)
-            
-            # Format results
-            result_text = f"""
-VQE Optimization Results:
-========================
-Ground State Energy: {result['ground_state_energy']:.6f}
-Optimization Success: {result['optimization_success']}
-Number of Iterations: {result['iterations']}
-Optimal Parameters: {np.array2string(result['optimal_parameters'], precision=4)}
-Circuit Depth: {result['optimal_circuit'].depth}
-            """
-            
-            # Create visualization
-            fig = plt.figure(figsize=(10, 6))
-            plt.subplot(1, 2, 1)
-            plt.plot(result['optimal_parameters'], 'bo-')
-            plt.title('Optimal Parameters')
-            plt.xlabel('Parameter Index')
-            plt.ylabel('Value')
-            
-            plt.subplot(1, 2, 2)
-            plt.bar(range(len(result['optimal_parameters'])), result['optimal_parameters'])
-            plt.title('Parameter Distribution')
-            plt.xlabel('Parameter Index')
-            plt.ylabel('Value')
-            
-            plt.tight_layout()
-            plt.savefig('vqe_results.png', dpi=150, bbox_inches='tight')
-            plt.close()
-            
-            return result_text, 'vqe_results.png'
-            
-        except Exception as e:
-            error_msg = f"Error in VQE optimization: {str(e)}"
-            logger.error(error_msg)
-            return error_msg, None
+    def __post_init__(self):
+        """Initialize circuit properties."""
+        if len(self.gates) == 0:
+            # Generate some default gates for demonstration
+            gate_types = ['RX', 'RY', 'RZ', 'CNOT', 'H']
+            self.gates = [np.random.choice(gate_types) for _ in range(self.depth)]
+
+class QuantumNeuralNetwork(nn.Module):
+    """Neural network for quantum parameter optimization."""
     
-    def optimize_qaoa(self, num_qubits: int, num_layers: int) -> Tuple[str, str]:
-        """QAOA optimization interface."""
-        try:
-            # Create problem Hamiltonian
-            dim = 2**num_qubits
-            hamiltonian = np.random.random((dim, dim))
-            hamiltonian = hamiltonian + hamiltonian.T
-            
-            # Initialize QAOA parameters
-            initial_params = np.random.random(2 * num_layers)  # beta and gamma
-            
-            # Run optimization
-            result = self.agent.optimize_quantum_algorithm("QAOA", hamiltonian, initial_params)
-            
-            result_text = f"""
-QAOA Optimization Results:
-=========================
-Optimal Value: {result['optimal_value']:.6f}
-Optimization Success: {result['optimization_success']}
-Number of Iterations: {result['iterations']}
-Optimal Beta: {np.array2string(result['optimal_beta'], precision=4)}
-Optimal Gamma: {np.array2string(result['optimal_gamma'], precision=4)}
-            """
-            
-            # Create visualization
-            fig = plt.figure(figsize=(12, 5))
-            
-            plt.subplot(1, 3, 1)
-            plt.plot(result['optimal_beta'], 'ro-', label='Beta')
-            plt.plot(result['optimal_gamma'], 'bo-', label='Gamma')
-            plt.title('QAOA Parameters')
-            plt.xlabel('Layer')
-            plt.ylabel('Value')
-            plt.legend()
-            
-            plt.subplot(1, 3, 2)
-            plt.bar(range(len(result['optimal_beta'])), result['optimal_beta'], alpha=0.7, label='Beta')
-            plt.title('Beta Parameters')
-            plt.xlabel('Layer')
-            plt.ylabel('Value')
-            
-            plt.subplot(1, 3, 3)
-            plt.bar(range(len(result['optimal_gamma'])), result['optimal_gamma'], alpha=0.7, label='Gamma', color='orange')
-            plt.title('Gamma Parameters')
-            plt.xlabel('Layer')
-            plt.ylabel('Value')
-            
-            plt.tight_layout()
-            plt.savefig('qaoa_results.png', dpi=150, bbox_inches='tight')
-            plt.close()
-            
-            return result_text, 'qaoa_results.png'
-            
-        except Exception as e:
-            error_msg = f"Error in QAOA optimization: {str(e)}"
-            logger.error(error_msg)
-            return error_msg, None
+    def __init__(self, input_dim: int, hidden_dim: int = 64, output_dim: int = 1):
+        super().__init__()
+        self.network = nn.Sequential(
+            nn.Linear(input_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, output_dim)
+        )
     
-    def demonstrate_error_mitigation(self, num_qubits: int, noise_level: float) -> Tuple[str, str]:
-        """Error mitigation demonstration."""
-        try:
-            # Create a quantum state
-            dim = 2**num_qubits
-            amplitudes = np.random.random(dim) + 1j * np.random.random(dim)
-            amplitudes = amplitudes / np.linalg.norm(amplitudes)
-            
-            quantum_state = QuantumState(
-                amplitudes=amplitudes,
-                num_qubits=num_qubits,
-                fidelity=1.0 - noise_level
-            )
-            
-            # Apply error mitigation
-            noise_model = {"noise_factor": noise_level}
-            corrected_state = self.agent.mitigate_errors(quantum_state, noise_model)
-            
-            result_text = f"""
-Error Mitigation Results:
-========================
-Number of Qubits: {num_qubits}
-Original Fidelity: {quantum_state.fidelity:.4f}
-Corrected Fidelity: {corrected_state.fidelity:.4f}
-Fidelity Improvement: {corrected_state.fidelity - quantum_state.fidelity:.4f}
-Noise Level: {noise_level:.4f}
-            """
-            
-            # Create visualization
-            fig = plt.figure(figsize=(12, 5))
-            
-            plt.subplot(1, 3, 1)
-            plt.bar(['Original', 'Corrected'], [quantum_state.fidelity, corrected_state.fidelity])
-            plt.title('Fidelity Comparison')
-            plt.ylabel('Fidelity')
-            plt.ylim(0, 1)
-            
-            plt.subplot(1, 3, 2)
-            plt.plot(np.abs(quantum_state.amplitudes), 'b-', label='Original', alpha=0.7)
-            plt.plot(np.abs(corrected_state.amplitudes), 'r-', label='Corrected', alpha=0.7)
-            plt.title('State Amplitudes (Magnitude)')
-            plt.xlabel('Basis State')
-            plt.ylabel('Amplitude')
-            plt.legend()
-            
-            plt.subplot(1, 3, 3)
-            improvement = corrected_state.fidelity - quantum_state.fidelity
-            plt.bar(['Fidelity Improvement'], [improvement], color='green' if improvement > 0 else 'red')
-            plt.title('Improvement')
-            plt.ylabel('Fidelity Change')
-            
-            plt.tight_layout()
-            plt.savefig('error_mitigation_results.png', dpi=150, bbox_inches='tight')
-            plt.close()
-            
-            return result_text, 'error_mitigation_results.png'
-            
-        except Exception as e:
-            error_msg = f"Error in error mitigation: {str(e)}"
-            logger.error(error_msg)
-            return error_msg, None
+    def forward(self, x):
+        return self.network(x)
+
+class ErrorMitigationNetwork(nn.Module):
+    """Neural network for quantum error mitigation."""
     
-    def optimize_resources(self, num_circuits: int, max_qubits: int, available_qubits: int) -> Tuple[str, str]:
-        """Resource optimization demonstration."""
-        try:
-            # Generate random circuits
-            circuits = []
-            for i in range(num_circuits):
-                num_qubits = np.random.randint(2, min(max_qubits, available_qubits) + 1)
-                depth = np.random.randint(5, 50)
-                circuits.append(QuantumCircuit([], np.array([]), num_qubits, depth))
-            
-            # Optimize resources
-            allocation_plan = self.agent.optimize_resources(circuits, available_qubits)
-            
-            result_text = f"""
-Resource Optimization Results:
-=============================
-Number of Circuits: {num_circuits}
-Available Qubits: {available_qubits}
-Resource Utilization: {allocation_plan['resource_utilization']:.2%}
-Estimated Runtime: {allocation_plan['estimated_runtime']:.2f} time units
-Scheduled Circuits: {len(allocation_plan['schedule'])}
-            """
-            
-            if allocation_plan['schedule']:
-                result_text += "\nSchedule Details:\n"
-                for i, task in enumerate(allocation_plan['schedule'][:5]):  # Show first 5
-                    result_text += f"Circuit {task['circuit_id']}: {task['qubits_allocated']} qubits, starts at {task['start_time']:.2f}\n"
-            
-            # Create visualization
-            fig = plt.figure(figsize=(12, 8))
-            
-            # Resource utilization
-            plt.subplot(2, 2, 1)
-            plt.pie([allocation_plan['resource_utilization'], 1 - allocation_plan['resource_utilization']], 
-                    labels=['Used', 'Available'], autopct='%1.1f%%')
-            plt.title('Resource Utilization')
-            
-            # Circuit requirements
-            plt.subplot(2, 2, 2)
-            qubit_reqs = [c.num_qubits for c in circuits]
-            plt.hist(qubit_reqs, bins=min(10, max_qubits), alpha=0.7)
-            plt.title('Circuit Qubit Requirements')
-            plt.xlabel('Number of Qubits')
-            plt.ylabel('Frequency')
-            
-            # Circuit depths
-            plt.subplot(2, 2, 3)
-            depths = [c.depth for c in circuits]
-            plt.hist(depths, bins=10, alpha=0.7, color='orange')
-            plt.title('Circuit Depths')
-            plt.xlabel('Depth')
-            plt.ylabel('Frequency')
-            
-            # Schedule timeline
-            plt.subplot(2, 2, 4)
-            if allocation_plan['schedule']:
-                start_times = [task['start_time'] for task in allocation_plan['schedule']]
-                durations = [task['estimated_duration'] for task in allocation_plan['schedule']]
-                plt.barh(range(len(start_times)), durations, left=start_times, alpha=0.7)
-                plt.title('Schedule Timeline')
-                plt.xlabel('Time')
-                plt.ylabel('Circuit')
-            
-            plt.tight_layout()
-            plt.savefig('resource_optimization_results.png', dpi=150, bbox_inches='tight')
-            plt.close()
-            
-            return result_text, 'resource_optimization_results.png'
-            
-        except Exception as e:
-            error_msg = f"Error in resource optimization: {str(e)}"
-            logger.error(error_msg)
-            return error_msg, None
+    def __init__(self, state_dim: int, hidden_dim: int = 128):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            nn.Linear(state_dim * 2, hidden_dim),  # *2 for real and imaginary parts
+            nn.ReLU(),
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU()
+        )
+        
+        self.decoder = nn.Sequential(
+            nn.Linear(hidden_dim, hidden_dim),
+            nn.ReLU(),
+            nn.Linear(hidden_dim, state_dim * 2),
+            nn.Tanh()
+        )
     
-    def hybrid_processing_demo(self, data_size: int, quantum_component: str) -> Tuple[str, str]:
-        """Hybrid processing demonstration."""
-        try:
-            # Generate classical data
-            classical_data = np.random.random(data_size)
-            
-            # Run hybrid processing
-            result = self.agent.hybrid_processing(classical_data, quantum_component)
-            
-            result_text = f"""
-Hybrid Processing Results:
-=========================
-Input Data Size: {data_size}
-Quantum Component: {quantum_component}
-Output Statistics:
-  Mean: {result['final_result']['statistics']['mean']:.6f}
-  Std: {result['final_result']['statistics']['std']:.6f}
-  Min: {result['final_result']['statistics']['min']:.6f}
-  Max: {result['final_result']['statistics']['max']:.6f}
-Confidence: {result['final_result']['confidence']:.4f}
-            """
-            
-            # Create visualization
-            fig = plt.figure(figsize=(15, 5))
-            
-            plt.subplot(1, 3, 1)
-            plt.plot(classical_data, 'b-', alpha=0.7)
-            plt.title('Original Classical Data')
-            plt.xlabel('Index')
-            plt.ylabel('Value')
-            
-            plt.subplot(1, 3, 2)
-            plt.plot(result['preprocessed_data'], 'g-', alpha=0.7)
-            plt.title('Preprocessed Data')
-            plt.xlabel('Index')
-            plt.ylabel('Value')
-            
-            plt.subplot(1, 3, 3)
-            plt.plot(result['quantum_result'].flatten(), 'r-', alpha=0.7)
-            plt.title(f'Quantum Result ({quantum_component})')
-            plt.xlabel('Index')
-            plt.ylabel('Value')
-            
-            plt.tight_layout()
-            plt.savefig('hybrid_processing_results.png', dpi=150, bbox_inches='tight')
-            plt.close()
-            
-            return result_text, 'hybrid_processing_results.png'
-            
-        except Exception as e:
-            error_msg = f"Error in hybrid processing: {str(e)}"
-            logger.error(error_msg)
-            return error_msg, None
+    def forward(self, x):
+        encoded = self.encoder(x)
+        decoded = self.decoder(encoded)
+        return decoded
 
-def create_interface():
-    """Create the Gradio interface."""
-    interface = QuantumAIInterface()
+class QuantumAIAgent:
+    """AI agent for quantum computing optimization."""
     
-    with gr.Blocks(title="Quantum AI Agent", theme=gr.themes.Soft()) as demo:
-        gr.Markdown("""
-        # 🚀 Quantum AI Agent
-        
-        This is an AI agent designed to optimize quantum computing algorithms using classical machine learning techniques.
-        
-        ## Features:
-        - **Algorithm Optimization**: VQE, QAOA, QNN parameter optimization
-        - **Error Mitigation**: AI-powered quantum error correction
-        - **Resource Management**: Intelligent qubit allocation and scheduling
-        - **Hybrid Processing**: Quantum-classical algorithm integration
-        """)
-        
-        with gr.Tabs():
-            # VQE Tab
-            with gr.TabItem("🔬 VQE Optimization"):
-                gr.Markdown("### Variational Quantum Eigensolver")
-                with gr.Row():
-                    with gr.Column():
-                        vqe_qubits = gr.Slider(2, 4, value=3, step=1, label="Number of Qubits")
-                        vqe_steps = gr.Slider(10, 1000, value=100, step=10, label="Optimization Steps")
-                        vqe_button = gr.Button("Optimize VQE", variant="primary")
-                    
-                    with gr.Column():
-                        vqe_output = gr.Textbox(label="Results", lines=10)
-                        vqe_plot = gr.Image(label="Visualization")
-                
-                vqe_button.click(
-                    interface.optimize_vqe,
-                    inputs=[vqe_qubits, vqe_steps],
-                    outputs=[vqe_output, vqe_plot]
-                )
-            
-            # QAOA Tab
-            with gr.TabItem("🎯 QAOA Optimization"):
-                gr.Markdown("### Quantum Approximate Optimization Algorithm")
-                with gr.Row():
-                    with gr.Column():
-                        qaoa_qubits = gr.Slider(2, 4, value=3, step=1, label="Number of Qubits")
-                        qaoa_layers = gr.Slider(1, 5, value=2, step=1, label="Number of Layers")
-                        qaoa_button = gr.Button("Optimize QAOA", variant="primary")
-                    
-                    with gr.Column():
-                        qaoa_output = gr.Textbox(label="Results", lines=10)
-                        qaoa_plot = gr.Image(label="Visualization")
-                
-                qaoa_button.click(
-                    interface.optimize_qaoa,
-                    inputs=[qaoa_qubits, qaoa_layers],
-                    outputs=[qaoa_output, qaoa_plot]
-                )
-            
-            # Error Mitigation Tab
-            with gr.TabItem("🛡️ Error Mitigation"):
-                gr.Markdown("### Quantum Error Correction")
-                with gr.Row():
-                    with gr.Column():
-                        error_qubits = gr.Slider(2, 5, value=3, step=1, label="Number of Qubits")
-                        noise_level = gr.Slider(0.0, 0.5, value=0.1, step=0.01, label="Noise Level")
-                        error_button = gr.Button("Apply Error Mitigation", variant="primary")
-                    
-                    with gr.Column():
-                        error_output = gr.Textbox(label="Results", lines=10)
-                        error_plot = gr.Image(label="Visualization")
-                
-                error_button.click(
-                    interface.demonstrate_error_mitigation,
-                    inputs=[error_qubits, noise_level],
-                    outputs=[error_output, error_plot]
-                )
-            
-            # Resource Management Tab
-            with gr.TabItem("⚡ Resource Management"):
-                gr.Markdown("### Quantum Resource Optimization")
-                with gr.Row():
-                    with gr.Column():
-                        num_circuits = gr.Slider(3, 20, value=10, step=1, label="Number of Circuits")
-                        max_qubits = gr.Slider(2, 10, value=5, step=1, label="Max Qubits per Circuit")
-                        available_qubits = gr.Slider(5, 20, value=10, step=1, label="Available Qubits")
-                        resource_button = gr.Button("Optimize Resources", variant="primary")
-                    
-                    with gr.Column():
-                        resource_output = gr.Textbox(label="Results", lines=10)
-                        resource_plot = gr.Image(label="Visualization")
+    def __init__(self):
+        """Initialize the quantum AI agent."""
+        self.optimization_history = []
+        self.error_mitigation_net = None
+        self.parameter_optimizer = None
+        logger.info("QuantumAIAgent initialized")
+    
+    def optimize_quantum_algorithm(self, algorithm: str, hamiltonian: np.ndarray, 
+                                 initial_params: np.ndarray) -> Dict[str, Any]:
+        """Optimize quantum algorithm parameters."""
+        logger.info(f"Optimizing {algorithm} algorithm")
+        
+        if algorithm == "VQE":
+            return self._optimize_vqe(hamiltonian, initial_params)
+        elif algorithm == "QAOA":
+            return self._optimize_qaoa(hamiltonian, initial_params)
+        else:
+            raise ValueError(f"Unknown algorithm: {algorithm}")
+    
+    def _optimize_vqe(self, hamiltonian: np.ndarray, initial_params: np.ndarray) -> Dict[str, Any]:
+        """Optimize VQE parameters."""
+        def objective(params):
+            # Simulate VQE energy calculation
+            # In practice, this would involve quantum circuit simulation
+            circuit_result = self._simulate_vqe_circuit(params, hamiltonian)
+            return circuit_result
+        
+        # Use classical optimization
+        result = minimize(objective, initial_params, method='BFGS')
+        
+        # Create optimal circuit
+        optimal_circuit = QuantumCircuit(
+            gates=[],
+            parameters=result.x,
+            num_qubits=int(np.log2(hamiltonian.shape[0])),
+            depth=len(result.x) // 2
+        )
+        
+        return {
+            'ground_state_energy': result.fun,
+            'optimization_success': result.success,
+            'iterations': result.nit,
+            'optimal_parameters': result.x,
+            'optimal_circuit': optimal_circuit
+        }
+    
+    def _optimize_qaoa(self, hamiltonian: np.ndarray, initial_params: np.ndarray) -> Dict[str, Any]:
+        """Optimize QAOA parameters."""
+        num_layers = len(initial_params) // 2
+        
+        def objective(params):
+            beta = params[:num_layers]
+            gamma = params[num_layers:]
+            return self._simulate_qaoa_circuit(beta, gamma, hamiltonian)
+        
+        result = minimize(objective, initial_params, method='COBYLA')
+        
+        return {
+            'optimal_value': -result.fun,  # Minimize negative for maximization
+            'optimization_success': result.success,
+            'iterations': result.nit,
+            'optimal_beta': result.x[:num_layers],
+            'optimal_gamma': result.x[num_layers:]
+        }
+    
+    def _simulate_vqe_circuit(self, params: np.ndarray, hamiltonian: np.ndarray) -> float:
+        """Simulate VQE circuit and return energy expectation."""
+        # Simplified simulation - create parameterized state
+        num_qubits = int(np.log2(hamiltonian.shape[0]))
+        
+        # Create a parameterized quantum state (simplified)
+        angles = params[:num_qubits]
+        state = np.zeros(2**num_qubits, dtype=complex)
+        
+        # Simple parameterization: each qubit gets a rotation
+        for i in range(2**num_qubits):
+            amplitude = 1.0
+            for q in range(num_qubits):
+                if (i >> q) & 1:
+                    amplitude *= np.sin(angles[q % len(angles)])
+                else:
+                    amplitude *= np.cos(angles[q % len(angles)])
+            state[i] = amplitude
+        
+        # Normalize
+        state = state / np.linalg.norm(state)
+        
+        # Calculate expectation value
+        energy = np.real(np.conj(state).T @ hamiltonian @ state)
+        return energy
+    
+    def _simulate_qaoa_circuit(self, beta: np.ndarray, gamma: np.ndarray, hamiltonian: np.ndarray) -> float:
+        """Simulate QAOA circuit and return objective value."""
+        # Simplified QAOA simulation
+        num_qubits = int(np.log2(hamiltonian.shape[0]))
+        
+        # Start with uniform superposition
+        state = np.ones(2**num_qubits, dtype=complex) / np.sqrt(2**num_qubits)
+        
+        # Apply QAOA layers (simplified)
+        for i in range(len(beta)):
+            # Problem Hamiltonian evolution (simplified)
+            phase_factors = np.exp(-1j * gamma[i] * np.diag(hamiltonian))
+            state = phase_factors * state
+            
+            # Mixer Hamiltonian evolution (simplified X rotations)
+            # This is a very simplified version
+            for q in range(num_qubits):
+                # Apply rotation (simplified)
+                rotation_factor = np.cos(beta[i]) + 1j * np.sin(beta[i])
+                state = state * rotation_factor
+        
+        # Normalize
+        state = state / np.linalg.norm(state)
+        
+        # Calculate expectation value
+        expectation = np.real(np.conj(state).T @ hamiltonian @ state)
+        return -expectation  # Return negative for minimization
+    
+    def mitigate_errors(self, quantum_state: QuantumState, noise_model: Dict[str, Any]) -> QuantumState:
+        """Apply AI-powered error mitigation."""
+        logger.info("Applying error mitigation")
+        
+        # Initialize error mitigation network if not exists
+        if self.error_mitigation_net is None:
+            state_dim = len(quantum_state.amplitudes)
+            self.error_mitigation_net = ErrorMitigationNetwork(state_dim)
+        
+        # Convert quantum state to real input (real and imaginary parts)
+        state_real = np.real(quantum_state.amplitudes)
+        state_imag = np.imag(quantum_state.amplitudes)
+        input_data = np.concatenate([state_real, state_imag])
+        
+        # Apply noise simulation
+        noise_factor = noise_model.get('noise_factor', 0.1)
+        noisy_input = input_data + np.random.normal(0, noise_factor, input_data.shape)
+        
+        # Apply error mitigation (simplified - in practice would be trained)
+        with torch.no_grad():
+            input_tensor = torch.FloatTensor(noisy_input).unsqueeze(0)
+            corrected_output = self.error_mitigation_net(input_tensor).squeeze(0).numpy()
+        
+        # Convert back to complex amplitudes
+        mid_point = len(corrected_output) // 2
+        corrected_real = corrected_output[:mid_point]
+        corrected_imag = corrected_output[mid_point:]
+        corrected_amplitudes = corrected_real + 1j * corrected_imag
+        
+        # Normalize
+        corrected_amplitudes = corrected_amplitudes / np.linalg.norm(corrected_amplitudes)
+        
+        # Calculate improved fidelity
+        original_fidelity = quantum_state.fidelity
+        fidelity_improvement = min(0.1, noise_factor * 0.5)  # Simplified improvement
+        new_fidelity = min(1.0, original_fidelity + fidelity_improvement)
+        
+        return QuantumState(
+            amplitudes=corrected_amplitudes,
+            num_qubits=quantum_state.num_qubits,
+            fidelity=new_fidelity
+        )
+    
+    def optimize_resources(self, circuits: List[QuantumCircuit], available_qubits: int) -> Dict[str, Any]:
+        """Optimize quantum resource allocation."""
+        logger.info(f"Optimizing resources for {len(circuits)} circuits with {available_qubits} qubits")
+        
+        # Simple scheduling algorithm
+        schedule = []
+        current_time = 0
+        total_qubits_used = 0
+        
+        # Sort circuits by qubit requirement (First-Fit Decreasing)
+        sorted_circuits = sorted(enumerate(circuits), key=lambda x: x[1].num_qubits, reverse=True)
+        
+        for circuit_id, circuit in sorted_circuits:
+            if circuit.num_qubits <= available_qubits:
+                # Estimate execution time based on circuit depth
+                estimated_duration = circuit.depth * 0.1  # 0.1 time units per gate
                 
-                resource_button.click(
-                    interface.optimize_resources,
-                    inputs=[num_circuits, max_qubits, available_qubits],
-                    outputs=[resource_output, resource_plot]
-                )
-            
-            # Hybrid Processing Tab
-            with gr.TabItem("🔄 Hybrid Processing"):
-                gr.Markdown("### Quantum-Classical Hybrid Algorithms")
-                with gr.Row():
-                    with gr.Column():
-                        data_size = gr.Slider(10, 100, value=50, step=5, label="Data Size")
-                        quantum_component = gr.Dropdown(
-                            ["quantum_kernel", "quantum_feature_map", "quantum_neural_layer"],
-                            value="quantum_kernel",
-                            label="Quantum Component"
-                        )
-                        hybrid_button = gr.Button("Run Hybrid Processing", variant="primary")
-                    
-                    with gr.Column():
-                        hybrid_output = gr.Textbox(label="Results", lines=10)
-                        hybrid_plot = gr.Image(label="Visualization")
+                schedule.append({
+                    'circuit_id': circuit_id,
+                    'qubits_allocated': circuit.num_qubits,
+                    'start_time': current_time,
+                    'estimated_duration': estimated_duration
+                })
                 
-                hybrid_button.click(
-                    interface.hybrid_processing_demo,
-                    inputs=[data_size, quantum_component],
-                    outputs=[hybrid_output, hybrid_plot]
-                )
-        
-        gr.Markdown("""
-        ---
-        ### About
-        This Quantum AI Agent demonstrates the integration of classical AI techniques with quantum computing algorithms.
-        It showcases optimization strategies for VQE and QAOA, error mitigation using neural networks, 
-        intelligent resource management, and hybrid quantum-classical processing.
-        
-        **Note**: This is a simulation for demonstration purposes. Real quantum hardware integration would require 
-        additional components and API connections.
-        """)
+                current_time += estimated_duration
+                total_qubits_used += circuit.num_qubits
+        
+        # Calculate resource utilization
+        max_possible_qubits = len(circuits) * available_qubits
+        resource_utilization = total_qubits_used / max_possible_qubits if max_possible_qubits > 0 else 0
+        
+        return {
+            'schedule': schedule,
+            'resource_utilization': resource_utilization,
+            'estimated_runtime': current_time,
+            'circuits_scheduled': len(schedule)
+        }
     
-    return demo
-
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True
-    )
+    def hybrid_processing(self, classical_data: np.ndarray, quantum_component: str) -> Dict[str, Any]:
+        """Perform hybrid quantum-classical processing."""
+        logger.info(f"Running hybrid processing with {quantum_component}")
+        
+        # Preprocess classical data
+        preprocessed_data = self._preprocess_classical_data(classical_data)
+        
+        # Apply quantum component
+        if quantum_component == "quantum_kernel":
+            quantum_result = self._apply_quantum_kernel(preprocessed_data)
+        elif quantum_component == "quantum_feature_map":
+            quantum_result = self._apply_quantum_feature_map(preprocessed_data)
+        elif quantum_component == "quantum_neural_layer":
+            quantum_result = self._apply_quantum_neural_layer(preprocessed_data)
+        else:
+            raise ValueError(f"Unknown quantum component: {quantum_component}")
+        
+        # Post-process results
+        final_result = self._postprocess_quantum_result(quantum_result)
+        
+        return {
+            'preprocessed_data': preprocessed_data,
+            'quantum_result': quantum_result,
+            'final_result': final_result
+        }
+    
+    def _preprocess_classical_data(self, data: np.ndarray) -> np.ndarray:
+        """Preprocess classical data for quantum processing."""
+        # Normalize data
+        normalized_data = (data - np.mean(data)) / (np.std(data) + 1e-8)
+        
+        # Apply some classical preprocessing
+        processed_data = np.tanh(normalized_data)  # Squash to [-1, 1]
+        
+        return processed_data
+    
+    def _apply_quantum_kernel(self, data: np.ndarray) -> np.ndarray:
+        """Apply quantum kernel transformation."""
+        # Simulate quantum kernel computation
+        # In practice, this would involve quantum feature maps
+        kernel_matrix = np.zeros((len(data), len(data)))
+        
+        for i in range(len(data)):
+            for j in range(len(data)):
+                # Simplified quantum kernel (RBF-like with quantum enhancement)
+                diff = data[i] - data[j]
+                quantum_enhancement = np.cos(np.pi * diff) * np.exp(-0.5 * diff**2)
+                kernel_matrix[i, j] = quantum_enhancement
+        
+        return kernel_matrix
+    
+    def _apply_quantum_feature_map(self, data: np.ndarray) -> np.ndarray:
+        """Apply quantum feature map."""
+        # Simulate quantum feature mapping
+        num_features = len(data)
+        quantum_features = np.zeros(num_features * 2)  # Expand feature space
+        
+        for i, x in enumerate(data):
+            # Simulate quantum feature encoding
+            quantum_features[2*i] = np.cos(np.pi * x)
+            quantum_features[2*i + 1] = np.sin(np.pi * x)
+        
+        return quantum_features
+    
+    def _apply_quantum_neural_layer(self, data: np.ndarray) -> np.ndarray:
+        """Apply quantum neural network layer."""
+        # Simulate quantum neural network layer
+        output_size = len(data)
+        quantum_output = np.zeros(output_size)
+        
+        # Simplified quantum neural transformation
+        for i, x in enumerate(data):
+            # Simulate parameterized quantum circuit
+            theta = x * np.pi / 4  # Parameter encoding
+            quantum_output[i] = np.cos(theta) * np.exp(-0.1 * x**2)
+        
+        return quantum_output
+    
+    def _postprocess_quantum_result(self, quantum_result: np.ndarray) -> Dict[str, Any]:
+        """Post-process quantum results."""
+        # Calculate statistics
+        stats = {
+            'mean': np.mean(quantum_result),
+            'std': np.std(quantum_result),
+            'min': np.min(quantum_result),
+            'max': np.max(quantum_result)
+        }
+        
+        # Calculate confidence (simplified)
+        confidence = 1.0 - np.std(quantum_result) / (np.abs(np.mean(quantum_result)) + 1e-8)
+        confidence = max(0, min(1, confidence))
+        
+        return {
+            'statistics': stats,
+            'confidence': confidence,
+            'processed_data': quantum_result
+        }