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 )