Quantum Annealing for Combinatorial Optimization: Foundations, Architectures, Benchmarks, and Emerging Directions

• URL: http://arxiv.org/abs/2602.03101v1
• Date: Tue, 03 Feb 2026 04:51:26 GMT
• Title: Quantum Annealing for Combinatorial Optimization: Foundations, Architectures, Benchmarks, and Emerging Directions
• Authors: Rudraksh Sharma, Ravi Katukam, Arjun Nagulapally,
• Abstract summary: Critical decision-making issues in science, engineering, and industry are based on optimization.<n>We develop a unified framework, relating adiabatic quantum dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to modern flux-qubit annealers.<n>We find that overhead in embedding and encoding is the largest of the scalability and performance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Critical decision-making issues in science, engineering, and industry are based on combinatorial optimization; however, its application is inherently limited by the NP-hard nature of the problem. A specialized paradigm of analogue quantum computing, quantum annealing (QA), has been proposed to solve these problems by encoding optimization problems into physical energy landscapes and solving them by quantum tunnelling systematically through exploration of solution space. This is a critical review that summarizes the current applications of quantum annealing to combinatorial optimization and includes a theoretical background, hardware designs, algorithm implementation strategies, encoding and embedding schemes, protocols to benchmark quantum annealing, areas of implementation, and links with the quantum algorithms implementation with gate-based hardware and classical solvers. We develop a unified framework, relating adiabatic quantum dynamics, Ising and QUBO models, stoquastic and non-stoquastic Hamiltonians, and diabatic transitions to modern flux-qubit annealers (Chimera, Pegasus, Zephyr topologies), and emergent architectures (Lechner-Hauke-Zoller systems, Rydberg atom platforms), and hybrids of quantum and classical computation. Through our analysis, we find that overhead in embedding and encoding is the largest determinant of the scalability and performance (this is not just the number of qubits). Minor embeddings also usually have a physical qubit count per logical variable of between 5 and 12 qubits, which limits effective problem capacity by 80-92% and, due to chain-breaking errors, compromises the quality of solutions.

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