Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems
This addresses computational bottlenecks in optimization for domains such as logistics and manufacturing, though it appears incremental as it builds on existing hybrid approaches.
The paper tackled large-scale mixed-integer programming problems by proposing hybrid quantum-classical algorithms, achieving high computational efficiency in solution quality and time across applications like molecular conformation and vehicle routing.
Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.