An Automated Toolchain for QUBO-based Optimization with Quantum-inspired Annealers
Yunting Zhang, Chin-Fu Nien, Chia-Wei Lin, Wen-Jui Chao, Chen-Yu Liu, Lien-Po Yu, Yuan‐Ho Chen · 2023
Recently, quantum computers have drawn attention to their potential to solve problems faster than classical computers. However, quantum hardware’s limited practicality and scalability have led to an interest in alternative approaches to solving optimization problems. One such approach is classical quantum-inspired annealers, which provide efficient and scalable solutions for combinatorial optimization problems (COPs) using classical hardware. To use annealers, COPs must be formulated as quadratic unconstrained binary optimization (QUBO) forms. Current tools require coding expertise and manual parameter tuning, posing barriers to entry. To address these challenges, we developed a user-friendly software toolchain that offers several advantages. Our toolchain features a friendly input and output interface, an automated parameter tuner, and a library of commonly encountered COPs. Our software toolchain’s accessibility promotes the use of quantum-inspired annealers and accelerates the development of practical solutions for real-world problems.