Optimized Quantum Embedding: A Universal Minor-Embedding Framework for Large Complete Bipartite Graph

Abstract

Minor embedding is essential for mapping largescale combinatorial problems onto quantum annealers, particularly in quantum machine learning and optimization. This work presents an optimized, universal minor-embedding framework that efficiently accommodates complete bipartite graphs onto the hardware topology of quantum annealers. By leveraging the inherent topographical periodicity of the physical quantum adiabatic annealer processor, our method systematically reduces qubit chain lengths, resulting in enhanced stability, computational efficiency, and scalability of quantum annealing. We benchmark our embedding framework against Minorminer, the default heuristic embedding algorithm, for the Pegasus topology, demonstrating that our approach significantly improves embedding quality. Our empirical results show a 99.98% reduction in embedding time for a 120 x 120 complete bipartite graphs. Additionally, our method eliminates long qubit chains, which primarily cause decoherence and computational errors in quantum annealing. These findings advance the scalability of quantum embeddings, particularly for quantum generative models, anomaly detection, and large-scale optimization tasks. Our results establish a foundation for integrating efficient quantum-classical hybrid solutions, paving the way for practical applications in quantum-enhanced machine learning and optimization.