强化学习辅助递归 QAOA

IF 5.8 2区 物理与天体物理 Q1 OPTICS EPJ Quantum Technology Pub Date : 2024-01-17 DOI:10.1140/epjqt/s40507-023-00214-w
Yash J. Patel, Sofiene Jerbi, Thomas Bäck, Vedran Dunjko
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引用次数: 0

摘要

近年来,量子逼近优化算法(QAOA)等变量子算法越来越受欢迎,因为它们为使用 NISQ 设备解决困难的组合优化问题带来了希望。然而,众所周知,在低深度时,QAOA 的某些局部性约束会限制其性能。为了超越这些限制,有人提出了 QAOA 的非局部变体,即递归 QAOA(RQAOA),以提高近似解的质量。与 QAOA 相比,对 RQAOA 的研究相对较少,而且人们对 RQAOA 的了解也较少,例如,对于哪类实例 RQAOA 可能无法提供高质量的解。不过,由于我们要解决的是 NP 难问题(特别是伊辛自旋模型),预计 RQAOA 确实会失败,这就提出了为组合优化设计更好的量子算法的问题。本着这一精神,我们识别并分析了(深度-1)RQAOA 失效的情况,并在此基础上提出了一种强化学习增强型 RQAOA 变体(RL-RQAOA),它在 RQAOA 的基础上进行了改进。我们的研究表明,RL-RQAOA 的性能比 RQAOA 有所提高:RL-RQAOA 在这些已确定的 RQAOA 性能较差的实例中表现得更好,而在 RQAOA 接近最优的实例中表现类似。我们的工作体现了强化学习和量子(启发)优化在为复杂问题设计新的、甚至更好的启发式算法方面潜在的有益协同作用。
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Reinforcement learning assisted recursive QAOA

In recent years, variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) 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 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 (depth-1) 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 complex problems.

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来源期刊
EPJ Quantum Technology
EPJ Quantum Technology Physics and Astronomy-Atomic and Molecular Physics, and Optics
CiteScore
7.70
自引率
7.50%
发文量
28
审稿时长
71 days
期刊介绍: Driven by advances in technology and experimental capability, the last decade has seen the emergence of quantum technology: a new praxis for controlling the quantum world. It is now possible to engineer complex, multi-component systems that merge the once distinct fields of quantum optics and condensed matter physics. EPJ Quantum Technology covers theoretical and experimental advances in subjects including but not limited to the following: Quantum measurement, metrology and lithography Quantum complex systems, networks and cellular automata Quantum electromechanical systems Quantum optomechanical systems Quantum machines, engineering and nanorobotics Quantum control theory Quantum information, communication and computation Quantum thermodynamics Quantum metamaterials The effect of Casimir forces on micro- and nano-electromechanical systems Quantum biology Quantum sensing Hybrid quantum systems Quantum simulations.
期刊最新文献
On the randomness of time ordered quantum measurements An investigation of errors in ellipse-fitting for cold-atom interferometers Numerical model of N-level cascade systems for atomic Radio Frequency sensing applications Electromagnetic side-channel attack risk assessment on a practical quantum-key-distribution receiver based on multi-class classification KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search
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