量子优化机器在工业应用中的准备
Readiness of Quantum Optimization Machines for Industrial Applications
摘要：
已经有多次尝试证明量子退火，特别是量子退火机上的量子退火，有可能优于CMOS技术上实现的当前经典优化算法。这些设备的基准测试一直存在争议。最初，使用随机自旋玻璃问题，然而，这些很快就被证明不适合检测任何量子加速。随后，基准测试转向精心设计的合成问题，旨在突出硬件的量子性质，同时（通常）确保经典优化技术在这些问题上表现不佳。更糟糕的是，迄今为止，与经典优化技术相比，问题变量数量的改进缩放的真正迹象仍然难以捉摸。在这里，我们分析了量子退火机对现实世界应用问题的准备情况。这些通常不是随机的，并且具有难以在合成基准中捕获的底层结构，因此对经典和量子优化技术都提出了意想不到的挑战。我们提出了一个全面的计算缩放分析故障诊断数字电路，考虑架构超越D波量子退火。我们发现，从乘法器电路中的真实数据产生的实例比其他具有可比数量的变量的代表性随机自旋玻璃基准更难。虽然我们的研究结果表明，横向场量子退火优于最先进的经典优化算法，但这些基准实例很难，输入量也很小，因此代表了第一个工业应用，非常适合测试近期量子退火机和其他优化问题的量子算法策略。
英文摘要：
There have been multiple attempts to demonstrate that quantum annealing and, in particular, quantum annealing on quantum annealing machines, has the potential to outperform current classical optimization algorithms implemented on CMOS technologies. The benchmarking of these devices has been controversial. Initially, random spin-glass problems were used, however, these were quickly shown to be not well suited to detect any quantum speedup. Subsequently, benchmarking shifted to carefully crafted synthetic problems designed to highlight the quantum nature of the hardware while (often) ensuring that classical optimization techniques do not perform well on them. Even worse, to date a true sign of improved scaling with the number of problem variables remains elusive when compared to classical optimization techniques. Here, we analyze the readiness of quantum annealing machines for real-world application problems. These are typically not random and have an underlying structure that is hard to capture in synthetic benchmarks, thus posing unexpected challenges for optimization techniques, both classical and quantum alike. We present a comprehensive computational scaling analysis of fault diagnosis in digital circuits, considering architectures beyond D-wave quantum annealers. We find that the instances generated from real data in multiplier circuits are harder than other representative random spin-glass benchmarks with a comparable number of variables. Although our results show that transverse-field quantum annealing is outperformed by state-of-the-art classical optimization algorithms, these benchmark instances are hard and small in the size of the input, therefore representing the first industrial application ideally suited for testing near-term quantum annealers and other quantum algorithmic strategies for optimization problems.
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