Ravi Katukam | Searcharxiv

arXiv2026

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

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.

quant-ph↗

arXiv2026

Bridging the Linear-Quadratic Gap: A Quantum-Classical Hybrid Approach to Robust Supply Chain Design

The design of supply chain networks in densely populated urban logistics systems faces a timely dilemma: the traditional optimisation approaches are effective to maximise the level of demand perfusion, but they are limited to embracing large expenses in overlapping the facilities and cannibalisation in the market. When tested on a high-fidelity digital twin of the Delhi NCR road network of thirty candidate sites, we establish that Classical Greedy algorithms using the theoretical maximum demand of (473 units) lack any theoretical overlap penalty, but incur a prohibitive overlap penalty (5.08). Here, in comparison, the Quantum-Inspired solution only losses 3.2% of demand (450 compared to 465 units relative to the optimal solution), but the solution preserves 21.8% less operational overlap risk (3.26 compared to 4.17), which can be viewed as a 35.8% improvement compared to the Greedy solution. Geospatial analysis shows that it can be attributed to a shift in strategies: This, in contrast to Classical approaches, which focus on locating facilities in the high-density central areas (North/Central Delhi), the quantum-inspired solver autonomously chooses the diversified topology of the North-south network, penetrating into the underserved periphery growth markets. This is a spatially balanced arrangement which is congruent to the polycentric structure of modern time megacities, and displays better stability to volatility in demand. We have shown that quantum-inspired optimisation methods can close the so-called Linear-Quadratic Gap phenomenon, i.e. the systematic inability of greedy methods to capture the actual quadratic interactions between facilities, and offer a way of computing the pathway to operationally robust and risk-optimised supply chain networks in dense urban conditions.

physics.soc-ph↗