量子近似优化：计算智能视角
Quantum Approximate Optimization: A Computational Intelligence Perspective
AI总结：
本文从计算智能视角介绍量子近似优化方法，阐释变分量子算法与QAOA的原理、关联领域及改进方向，给出典型组合优化问题的实现模板，助力计算智能从业者应用相关技术。
摘要：
量子计算是物理学、工程学与计算机科学交叉的新兴领域，有望对计算智能（CI）产生重大影响。鉴于量子近似优化方法与组合问题求解直接相关，本文旨在向计算智能领域介绍这类方法。我们首先介绍量子计算与变分量子算法（VQAs），VQAs是在含噪声中等规模量子（NISQ）设备上近期实现量子解决方案的有效方法，这类设备的量子比特可靠性较低、纠错技术处于早期阶段。随后，我们阐释Farhi等人提出的量子近似优化算法（Farhi's QAOA，以避免混淆），该变分量子算法经Hadfield等人推广为量子交替算子拟设（QAOA），这是一种受自然启发（具体为绝热启发）的量子元启发式算法，用于在基于门的量子计算机上近似求解组合优化问题。我们讨论了QAOA与计算学习理论、遗传算法等相关领域的关联，梳理了混合量子-经典智能系统的现有技术与已知成果，给出了QAOA的构建示意图，还探讨了如何利用计算智能技术改进QAOA。最后，我们介绍了QAOA在著名的最大割、最大二等分与旅行商问题上的实现，可供有兴趣使用QAOA的计算智能从业者作为参考模板。
英文摘要：
Quantum computing is an emerging field on the multidisciplinary interface between physics, engineering, and computer science with the potential to make a large impact on computational intelligence (CI). The aim of this paper is to introduce quantum approximate optimization methods to the CI community because of direct relevance to solving combinatorial problems. We introduce quantum computing and variational quantum algorithms (VQAs). VQAs are an effective method for the near-term implementation of quantum solutions on noisy intermediate-scale quantum (NISQ) devices with less reliable qubits and early-stage error correction. Then, we explain Farhi et al.'s quantum approximate optimization algorithm (Farhi's QAOA, to prevent confusion). This VQA is generalized by Hadfield et al. to the quantum alternating operator ansatz (QAOA), which is a nature-inspired (particularly, adiabatic) quantum metaheuristic for approximately solving combinatorial optimization problems on gate-based quantum computers. We discuss connections of QAOA to relevant domains, such as computational learning theory and genetic algorithms, discussing current techniques and known results regarding hybrid quantum-classical intelligence systems. We present a schematic of how QAOA is constructed, and also discuss how CI techniques can be used to improve QAOA. We conclude with QAOA implementations for the well-known maximum cut, maximum bisection, and traveling salesperson problems, which can serve as templates for CI practitioners interested in using QAOA.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
