CCockrum/Quantum-Optimization-Agent
0
1import gradio as gr2import numpy as np3import torch4import json5import matplotlib.pyplot as plt6import plotly.graph_objects as go7import plotly.express as px8from typing import Dict, List, Tuple, Any9import logging10 11# Import your quantum AI agent (assuming it's in quantum_agent.py)12from quantum_agent import QuantumAIAgent, QuantumState, QuantumCircuit13 14# Configure logging15logging.basicConfig(level=logging.INFO)16logger = logging.getLogger(__name__)17 18class QuantumAIInterface:19 """Gradio interface for the Quantum AI Agent."""20 21 def __init__(self):22 self.agent = QuantumAIAgent()23 logger.info("Quantum AI Interface initialized")24 25 def optimize_vqe(self, num_qubits: int, optimization_steps: int) -> Tuple[str, str]:26 """VQE optimization interface."""27 try:28 # Create a random Hamiltonian29 dim = 2**num_qubits30 hamiltonian = np.random.random((dim, dim))31 hamiltonian = hamiltonian + hamiltonian.T # Make Hermitian32 33 # Initialize random parameters34 initial_params = np.random.random(num_qubits * 2)35 36 # Run optimization37 result = self.agent.optimize_quantum_algorithm("VQE", hamiltonian, initial_params)38 39 # Format results40 result_text = f"""41VQE Optimization Results:42========================43Ground State Energy: {result['ground_state_energy']:.6f}44Optimization Success: {result['optimization_success']}45Number of Iterations: {result['iterations']}46Optimal Parameters: {np.array2string(result['optimal_parameters'], precision=4)}47Circuit Depth: {result['optimal_circuit'].depth}48 """49 50 # Create visualization51 fig = plt.figure(figsize=(10, 6))52 plt.subplot(1, 2, 1)53 plt.plot(result['optimal_parameters'], 'bo-')54 plt.title('Optimal Parameters')55 plt.xlabel('Parameter Index')56 plt.ylabel('Value')57 58 plt.subplot(1, 2, 2)59 plt.bar(range(len(result['optimal_parameters'])), result['optimal_parameters'])60 plt.title('Parameter Distribution')61 plt.xlabel('Parameter Index')62 plt.ylabel('Value')63 64 plt.tight_layout()65 plt.savefig('vqe_results.png', dpi=150, bbox_inches='tight')66 plt.close()67 68 return result_text, 'vqe_results.png'69 70 except Exception as e:71 error_msg = f"Error in VQE optimization: {str(e)}"72 logger.error(error_msg)73 return error_msg, None74 75 def optimize_qaoa(self, num_qubits: int, num_layers: int) -> Tuple[str, str]:76 """QAOA optimization interface."""77 try:78 # Create problem Hamiltonian79 dim = 2**num_qubits80 hamiltonian = np.random.random((dim, dim))81 hamiltonian = hamiltonian + hamiltonian.T82 83 # Initialize QAOA parameters84 initial_params = np.random.random(2 * num_layers) # beta and gamma85 86 # Run optimization87 result = self.agent.optimize_quantum_algorithm("QAOA", hamiltonian, initial_params)88 89 result_text = f"""90QAOA Optimization Results:91=========================92Optimal Value: {result['optimal_value']:.6f}93Optimization Success: {result['optimization_success']}94Number of Iterations: {result['iterations']}95Optimal Beta: {np.array2string(result['optimal_beta'], precision=4)}96Optimal Gamma: {np.array2string(result['optimal_gamma'], precision=4)}97 """98 99 # Create visualization100 fig = plt.figure(figsize=(12, 5))101 102 plt.subplot(1, 3, 1)103 plt.plot(result['optimal_beta'], 'ro-', label='Beta')104 plt.plot(result['optimal_gamma'], 'bo-', label='Gamma')105 plt.title('QAOA Parameters')106 plt.xlabel('Layer')107 plt.ylabel('Value')108 plt.legend()109 110 plt.subplot(1, 3, 2)111 plt.bar(range(len(result['optimal_beta'])), result['optimal_beta'], alpha=0.7, label='Beta')112 plt.title('Beta Parameters')113 plt.xlabel('Layer')114 plt.ylabel('Value')115 116 plt.subplot(1, 3, 3)117 plt.bar(range(len(result['optimal_gamma'])), result['optimal_gamma'], alpha=0.7, label='Gamma', color='orange')118 plt.title('Gamma Parameters')119 plt.xlabel('Layer')120 plt.ylabel('Value')121 122 plt.tight_layout()123 plt.savefig('qaoa_results.png', dpi=150, bbox_inches='tight')124 plt.close()125 126 return result_text, 'qaoa_results.png'127 128 except Exception as e:129 error_msg = f"Error in QAOA optimization: {str(e)}"130 logger.error(error_msg)131 return error_msg, None132 133 def demonstrate_error_mitigation(self, num_qubits: int, noise_level: float) -> Tuple[str, str]:134 """Error mitigation demonstration."""135 try:136 # Create a quantum state137 dim = 2**num_qubits138 amplitudes = np.random.random(dim) + 1j * np.random.random(dim)139 amplitudes = amplitudes / np.linalg.norm(amplitudes)140 141 quantum_state = QuantumState(142 amplitudes=amplitudes,143 num_qubits=num_qubits,144 fidelity=1.0 - noise_level145 )146 147 # Apply error mitigation148 noise_model = {"noise_factor": noise_level}149 corrected_state = self.agent.mitigate_errors(quantum_state, noise_model)150 151 result_text = f"""152Error Mitigation Results:153========================154Number of Qubits: {num_qubits}155Original Fidelity: {quantum_state.fidelity:.4f}156Corrected Fidelity: {corrected_state.fidelity:.4f}157Fidelity Improvement: {corrected_state.fidelity - quantum_state.fidelity:.4f}158Noise Level: {noise_level:.4f}159 """160 161 # Create visualization162 fig = plt.figure(figsize=(12, 5))163 164 plt.subplot(1, 3, 1)165 plt.bar(['Original', 'Corrected'], [quantum_state.fidelity, corrected_state.fidelity])166 plt.title('Fidelity Comparison')167 plt.ylabel('Fidelity')168 plt.ylim(0, 1)169 170 plt.subplot(1, 3, 2)171 plt.plot(np.abs(quantum_state.amplitudes), 'b-', label='Original', alpha=0.7)172 plt.plot(np.abs(corrected_state.amplitudes), 'r-', label='Corrected', alpha=0.7)173 plt.title('State Amplitudes (Magnitude)')174 plt.xlabel('Basis State')175 plt.ylabel('Amplitude')176 plt.legend()177 178 plt.subplot(1, 3, 3)179 improvement = corrected_state.fidelity - quantum_state.fidelity180 plt.bar(['Fidelity Improvement'], [improvement], color='green' if improvement > 0 else 'red')181 plt.title('Improvement')182 plt.ylabel('Fidelity Change')183 184 plt.tight_layout()185 plt.savefig('error_mitigation_results.png', dpi=150, bbox_inches='tight')186 plt.close()187 188 return result_text, 'error_mitigation_results.png'189 190 except Exception as e:191 error_msg = f"Error in error mitigation: {str(e)}"192 logger.error(error_msg)193 return error_msg, None194 195 def optimize_resources(self, num_circuits: int, max_qubits: int, available_qubits: int) -> Tuple[str, str]:196 """Resource optimization demonstration."""197 try:198 # Generate random circuits199 circuits = []200 for i in range(num_circuits):201 num_qubits = np.random.randint(2, min(max_qubits, available_qubits) + 1)202 depth = np.random.randint(5, 50)203 circuits.append(QuantumCircuit([], np.array([]), num_qubits, depth))204 205 # Optimize resources206 allocation_plan = self.agent.optimize_resources(circuits, available_qubits)207 208 result_text = f"""209Resource Optimization Results:210=============================211Number of Circuits: {num_circuits}212Available Qubits: {available_qubits}213Resource Utilization: {allocation_plan['resource_utilization']:.2%}214Estimated Runtime: {allocation_plan['estimated_runtime']:.2f} time units215Scheduled Circuits: {len(allocation_plan['schedule'])}216 """217 218 if allocation_plan['schedule']:219 result_text += "\nSchedule Details:\n"220 for i, task in enumerate(allocation_plan['schedule'][:5]): # Show first 5221 result_text += f"Circuit {task['circuit_id']}: {task['qubits_allocated']} qubits, starts at {task['start_time']:.2f}\n"222 223 # Create visualization224 fig = plt.figure(figsize=(12, 8))225 226 # Resource utilization227 plt.subplot(2, 2, 1)228 plt.pie([allocation_plan['resource_utilization'], 1 - allocation_plan['resource_utilization']], 229 labels=['Used', 'Available'], autopct='%1.1f%%')230 plt.title('Resource Utilization')231 232 # Circuit requirements233 plt.subplot(2, 2, 2)234 qubit_reqs = [c.num_qubits for c in circuits]235 plt.hist(qubit_reqs, bins=min(10, max_qubits), alpha=0.7)236 plt.title('Circuit Qubit Requirements')237 plt.xlabel('Number of Qubits')238 plt.ylabel('Frequency')239 240 # Circuit depths241 plt.subplot(2, 2, 3)242 depths = [c.depth for c in circuits]243 plt.hist(depths, bins=10, alpha=0.7, color='orange')244 plt.title('Circuit Depths')245 plt.xlabel('Depth')246 plt.ylabel('Frequency')247 248 # Schedule timeline249 plt.subplot(2, 2, 4)250 if allocation_plan['schedule']:251 start_times = [task['start_time'] for task in allocation_plan['schedule']]252 durations = [task['estimated_duration'] for task in allocation_plan['schedule']]253 plt.barh(range(len(start_times)), durations, left=start_times, alpha=0.7)254 plt.title('Schedule Timeline')255 plt.xlabel('Time')256 plt.ylabel('Circuit')257 258 plt.tight_layout()259 plt.savefig('resource_optimization_results.png', dpi=150, bbox_inches='tight')260 plt.close()261 262 return result_text, 'resource_optimization_results.png'263 264 except Exception as e:265 error_msg = f"Error in resource optimization: {str(e)}"266 logger.error(error_msg)267 return error_msg, None268 269 def hybrid_processing_demo(self, data_size: int, quantum_component: str) -> Tuple[str, str]:270 """Hybrid processing demonstration."""271 try:272 # Generate classical data273 classical_data = np.random.random(data_size)274 275 # Run hybrid processing276 result = self.agent.hybrid_processing(classical_data, quantum_component)277 278 result_text = f"""279Hybrid Processing Results:280=========================281Input Data Size: {data_size}282Quantum Component: {quantum_component}283Output Statistics:284 Mean: {result['final_result']['statistics']['mean']:.6f}285 Std: {result['final_result']['statistics']['std']:.6f}286 Min: {result['final_result']['statistics']['min']:.6f}287 Max: {result['final_result']['statistics']['max']:.6f}288Confidence: {result['final_result']['confidence']:.4f}289 """290 291 # Create visualization292 fig = plt.figure(figsize=(15, 5))293 294 plt.subplot(1, 3, 1)295 plt.plot(classical_data, 'b-', alpha=0.7)296 plt.title('Original Classical Data')297 plt.xlabel('Index')298 plt.ylabel('Value')299 300 plt.subplot(1, 3, 2)301 plt.plot(result['preprocessed_data'], 'g-', alpha=0.7)302 plt.title('Preprocessed Data')303 plt.xlabel('Index')304 plt.ylabel('Value')305 306 plt.subplot(1, 3, 3)307 plt.plot(result['quantum_result'].flatten(), 'r-', alpha=0.7)308 plt.title(f'Quantum Result ({quantum_component})')309 plt.xlabel('Index')310 plt.ylabel('Value')311 312 plt.tight_layout()313 plt.savefig('hybrid_processing_results.png', dpi=150, bbox_inches='tight')314 plt.close()315 316 return result_text, 'hybrid_processing_results.png'317 318 except Exception as e:319 error_msg = f"Error in hybrid processing: {str(e)}"320 logger.error(error_msg)321 return error_msg, None322 323def create_interface():324 """Create the Gradio interface."""325 interface = QuantumAIInterface()326 327 with gr.Blocks(title="Quantum AI Agent", theme=gr.themes.Soft()) as demo:328 gr.Markdown("""329 # 🚀 Quantum AI Agent330 331 This is an AI agent designed to optimize quantum computing algorithms using classical machine learning techniques.332 333 ## Features:334 - **Algorithm Optimization**: VQE, QAOA, QNN parameter optimization335 - **Error Mitigation**: AI-powered quantum error correction336 - **Resource Management**: Intelligent qubit allocation and scheduling337 - **Hybrid Processing**: Quantum-classical algorithm integration338 """)339 340 with gr.Tabs():341 # VQE Tab342 with gr.TabItem("🔬 VQE Optimization"):343 gr.Markdown("### Variational Quantum Eigensolver")344 with gr.Row():345 with gr.Column():346 vqe_qubits = gr.Slider(2, 4, value=3, step=1, label="Number of Qubits")347 vqe_steps = gr.Slider(10, 1000, value=100, step=10, label="Optimization Steps")348 vqe_button = gr.Button("Optimize VQE", variant="primary")349 350 with gr.Column():351 vqe_output = gr.Textbox(label="Results", lines=10)352 vqe_plot = gr.Image(label="Visualization")353 354 vqe_button.click(355 interface.optimize_vqe,356 inputs=[vqe_qubits, vqe_steps],357 outputs=[vqe_output, vqe_plot]358 )359 360 # QAOA Tab361 with gr.TabItem("🎯 QAOA Optimization"):362 gr.Markdown("### Quantum Approximate Optimization Algorithm")363 with gr.Row():364 with gr.Column():365 qaoa_qubits = gr.Slider(2, 4, value=3, step=1, label="Number of Qubits")366 qaoa_layers = gr.Slider(1, 5, value=2, step=1, label="Number of Layers")367 qaoa_button = gr.Button("Optimize QAOA", variant="primary")368 369 with gr.Column():370 qaoa_output = gr.Textbox(label="Results", lines=10)371 qaoa_plot = gr.Image(label="Visualization")372 373 qaoa_button.click(374 interface.optimize_qaoa,375 inputs=[qaoa_qubits, qaoa_layers],376 outputs=[qaoa_output, qaoa_plot]377 )378 379 # Error Mitigation Tab380 with gr.TabItem("🛡️ Error Mitigation"):381 gr.Markdown("### Quantum Error Correction")382 with gr.Row():383 with gr.Column():384 error_qubits = gr.Slider(2, 5, value=3, step=1, label="Number of Qubits")385 noise_level = gr.Slider(0.0, 0.5, value=0.1, step=0.01, label="Noise Level")386 error_button = gr.Button("Apply Error Mitigation", variant="primary")387 388 with gr.Column():389 error_output = gr.Textbox(label="Results", lines=10)390 error_plot = gr.Image(label="Visualization")391 392 error_button.click(393 interface.demonstrate_error_mitigation,394 inputs=[error_qubits, noise_level],395 outputs=[error_output, error_plot]396 )397 398 # Resource Management Tab399 with gr.TabItem("⚡ Resource Management"):400 gr.Markdown("### Quantum Resource Optimization")401 with gr.Row():402 with gr.Column():403 num_circuits = gr.Slider(3, 20, value=10, step=1, label="Number of Circuits")404 max_qubits = gr.Slider(2, 10, value=5, step=1, label="Max Qubits per Circuit")405 available_qubits = gr.Slider(5, 20, value=10, step=1, label="Available Qubits")406 resource_button = gr.Button("Optimize Resources", variant="primary")407 408 with gr.Column():409 resource_output = gr.Textbox(label="Results", lines=10)410 resource_plot = gr.Image(label="Visualization")411 412 resource_button.click(413 interface.optimize_resources,414 inputs=[num_circuits, max_qubits, available_qubits],415 outputs=[resource_output, resource_plot]416 )417 418 # Hybrid Processing Tab419 with gr.TabItem("🔄 Hybrid Processing"):420 gr.Markdown("### Quantum-Classical Hybrid Algorithms")421 with gr.Row():422 with gr.Column():423 data_size = gr.Slider(10, 100, value=50, step=5, label="Data Size")424 quantum_component = gr.Dropdown(425 ["quantum_kernel", "quantum_feature_map", "quantum_neural_layer"],426 value="quantum_kernel",427 label="Quantum Component"428 )429 hybrid_button = gr.Button("Run Hybrid Processing", variant="primary")430 431 with gr.Column():432 hybrid_output = gr.Textbox(label="Results", lines=10)433 hybrid_plot = gr.Image(label="Visualization")434 435 hybrid_button.click(436 interface.hybrid_processing_demo,437 inputs=[data_size, quantum_component],438 outputs=[hybrid_output, hybrid_plot]439 )440 441 gr.Markdown("""442 ---443 ### About444 This Quantum AI Agent demonstrates the integration of classical AI techniques with quantum computing algorithms.445 It showcases optimization strategies for VQE and QAOA, error mitigation using neural networks, 446 intelligent resource management, and hybrid quantum-classical processing.447 448 **Note**: This is a simulation for demonstration purposes. Real quantum hardware integration would require 449 additional components and API connections.450 """)451 452 return demo453 454if __name__ == "__main__":455 demo = create_interface()456 demo.launch(457 server_name="0.0.0.0",458 server_port=7860,459 share=True460 )