Intelligently partition workloads between quantum and classical systems with AI-driven optimization. Achieve up to 67% cost savings while maintaining optimal performance across hybrid algorithms.
Machine learning algorithms automatically determine optimal workload distribution between quantum and classical resources.
Intelligent cost analysis and resource allocation to minimize expenses while maintaining performance requirements.
Build complex hybrid workflows with dependencies, parallel execution, and error handling across quantum and classical systems.
import { QuantumClassicalBridge, WorkloadPartitioner } from '@q-intercept/sdk';
// Initialize hybrid computing bridge
const bridge = new QuantumClassicalBridge({
quantumBackends: ['ibm_quantum', 'google_sycamore'],
classicalBackends: ['aws_batch', 'gcp_compute'],
optimizationLevel: 3,
autoPartitioning: true
});
// Define hybrid algorithm
const hybridAlgorithm = {
name: 'portfolio_optimization',
quantumParts: ['qaoa_optimization', 'variational_eigensolver'],
classicalParts: ['data_preprocessing', 'result_analysis'],
dataFlow: {
input: 'classical',
processing: 'hybrid',
output: 'classical'
}
};
// Execute with automatic workload partitioning
const result = await bridge.execute(hybridAlgorithm, {
data: portfolioData,
constraints: {
maxQuantumTime: 300000, // 5 minutes
maxCost: 50,
minAccuracy: 0.95
}
});
console.log(`Optimization completed in ${result.totalTime}ms`);
console.log(`Cost savings: ${result.costOptimization}%`);