Create app.py

app.py

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+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 logging
+
+# 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."""
+    
+    def __init__(self):
+        self.agent = QuantumAIAgent()
+        logger.info("Quantum AI Interface initialized")
+    
+    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 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 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 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 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 create_interface():
+    """Create the Gradio interface."""
+    interface = QuantumAIInterface()
+    
+    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")
+                
+                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")
+                
+                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.
+        """)
+    
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=True
+    )