Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its performance. To go beyond these limitations, a non-local variant of QAOA, namely recursive QAOA (RQAOA), was proposed to improve the quality of approximate solutions. The RQAOA has been studied comparatively less than QAOA, and it is less understood, for instance, for what family of instances it may fail to provide high quality solutions. However, as we are tackling $\mathsf{NP}$-hard problems (specifically, the Ising spin model), it is expected that RQAOA does fail, raising the question of designing even better quantum algorithms for combinatorial optimization. In this spirit, we identify and analyze cases where RQAOA fails and, based on this, propose a reinforcement learning enhanced RQAOA variant (RL-RQAOA) that improves upon RQAOA. We show that the performance of RL-RQAOA improves over RQAOA: RL-RQAOA is strictly better on these identified instances where RQAOA underperforms, and is similarly performing on instances where RQAOA is near-optimal. Our work exemplifies the potentially beneficial synergy between reinforcement learning and quantum (inspired) optimization in the design of new, even better heuristics for hard problems.
Modified Recursive QAOA for Exact Max-Cut Solutions on Bipartite Graphs: Closing the Gap Beyond QAOA Limit
Korea Institute for Advanced Study (KIAS)（韩国高等科学研究院）；Australian National University（澳大利亚国立大学）；Agency for Science, Technology and Research (A*STAR)（科学技术研究局）；Kyung Hee University（庆熙大学）
A Quantum-Assisted Agentic Distributed Artificial Intelligence Framework for Deadline-Bounded Orchestration of Hybrid Renewable Microgrids
Philips University（菲利普斯大学）；University of Cyprus（塞浦路斯大学）；CYENS Centre of Excellence（CYENS卓越中心）；Japan Advanced Institute of Science and Technology（日本先端科学技术大学院大学）；Doshisha University（同志社大学）
SAQC: A SAT-Aware Compilation Framework for QAOA-Based Quantum Optimization
DFKI, Bremen, Germany（德国弗劳恩霍夫协会第一智能系统研究所）；Institute of Computer Science, University of Bremen, Bremen, Germany（不来梅大学计算机科学研究所）；University of Bremen, Bremen, Germany（不来梅大学）
Classical and Quantum Heuristics for the Binary Paint Shop Problem
Centre for Quantum Technologies (CQT), National University of Singapore（新加坡国立大学量子技术中心）；Quantum Innovation Centre (Q.InC), Agency for Science, Technology and Research (A*STAR)（科技研究局量子创新中心）；Department of Quantum Science and Technology, Research School of Physics, Australian National University（澳大利亚国立大学物理研究学院量子科学与技术系）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（科技研究局高性能计算研究所）；Science, Mathematics and Technology Cluster, Singapore University of Technology and Design（新加坡科技设计大学科学技术与数学集群）；Engineering Cluster, Singapore Institute of Technology（新加坡理工学院工程集群）
Near-Optimal Parameter Tuning of Level-1 QAOA for Ising Models
National University of Singapore（新加坡国立大学）；Agency for Science, Technology and Research (A*STAR)（科技研究局）；Australian National University（澳大利亚国立大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Singapore Institute of Technology（新加坡理工学院）；Korea Institute for Advanced Study (KIAS)（韩国高等科学研究院）；Electronics and Telecommunications Research Institute (ETRI)（电子电信研究所）
Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area（粤港澳大湾区量子科学中心）；Department of Physics, Southern University of Science and Technology（南方科技大学物理系）
Beyond Quantum Advantage: Improved Classical Algorithms for the Binary Paint Shop Problem
Institute for Frontier Materials on Earth and in Space, German Aerospace Center (DLR)（地球与太空前沿材料研究所，德国航空航天中心）；University of Cologne（科隆大学）；Institute for Software Technology, Department High-Performance Computing, German Aerospace Center (DLR)（高性能计算系软件工程研究所，德国航空航天中心）
Fair sampling of ground-state configurations using hybrid quantum-classical MCMC algorithms
Graduate School of Engineering Science, The University of Osaka（大阪大学工学研究科）；Center for Quantum Information and Quantum Biology, The University of Osaka（大阪大学量子信息与量子生物学中心）；Center for Quantum Computing, RIKEN（理化学研究所量子计算中心）
Scaling Quantum Optimization to the Thousand-Qubit Scale with Distributed Quantum Sampling
Qoro Quantum Ltd.（Qoro Quantum有限公司）；Qoro Quantum GmbH（Qoro Quantum有限责任公司）；National Quantum Computing Centre（国家量子计算中心）；Quantum Software Lab, The University of Edinburgh（爱丁堡大学量子软件实验室）；Hartree Centre, STFC（STFC哈特里中心）；E.ON Digital Technology GmbH（E.ON数字技术有限责任公司）
