QAOA vs. 75,000 Nodes: Building a Hybrid Architecture to Solve NP-Hard Problems When Quantum Simulators Hit a Wall
The article discusses the challenges of using quantum computing to solve NP-hard problems, particularly with large datasets. It introduces a hybrid architecture that decomposes massive networks into smaller, manageable fragments for quantum processing. The author outlines the technical implementation and hurdles faced during the development of this system.
- ▪Quantum computing is currently in the NISQ era, facing limitations with algorithms like QAOA when dealing with large datasets.
- ▪The author developed a hybrid orchestrator that breaks down large graphs into clusters to optimize processing with quantum solvers.
- ▪Key components of the system include graph decomposition, a quantum solver for MaxCut problems, and an aggregation layer to compile results.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3943007) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } EmperoQ Posted on May 20 QAOA vs. 75,000 Nodes: Building a Hybrid Architecture to Solve NP-Hard Problems When Quantum Simulators Hit a Wall #python #quantumcomputing #opensource #ai Quantum computing today is firmly in the NISQ (Noisy Intermediate-Scale Quantum) era. In theory, everything sounds brilliant: quantum advantage, exponential speedup, and the ability to solve problems far beyond the reach of classical computers.
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