论文标题
使用量子计算机解决现实世界中的问题 - 今天可以实现什么?
Using a quantum computer to solve a real-world problem -- what can be achieved today?
论文作者
论文摘要
量子计算是一项重要的开发技术,有可能改变可以实际解决的科学和业务问题的景观。广泛的兴奋源自容忍量子计算机以解决以前棘手的问题的潜力。至少要等到2030年才能提供这样的机器。因此,我们目前处于所谓的NISQ时代,其中更多的启发式量子方法应用于量子硬件的早期版本。在本文中,我们试图对当前NISQ时代的量子计算的许多技术方面提供更容易访问的解释,探讨了2种主要的混合经典量子算法,QAOA和VQE以及量子退火。我们将这些方法应用于设施位置问题的形式的组合优化示例。探索的方法包括QAOA中不同类型的混合器(X,XY和新型3XY混合器)的应用,以及许多设置对重要元参数的影响,这些参数通常不集中在研究论文中。同样,我们在量子退火的上下文中探索替代参数设置。我们的研究证实,量子门硬件的能力比目前的规模和忠诚度更高,以便能够在商业上有价值的水平上解决此类问题。量子退火更接近提供量子优势,但还需要在规模和连接性方面取得显着提高,以解决经典解决方案是优化的优化问题。
Quantum computing is an important developing technology with the potential to revolutionise the landscape of scientific and business problems that can be practically addressed. The widespread excitement derives from the potential for a fault tolerant quantum computer to solve previously intractable problems. Such a machine is not expected to be available until 2030 at least. Thus we are currently in the so-called NISQ era where more heuristic quantum approaches are being applied to early versions of quantum hardware. In this paper we seek to provide a more accessible explanation of many of the more technical aspects of quantum computing in the current NISQ era exploring the 2 main hybrid classical-quantum algorithms, QAOA and VQE, as well as quantum annealing. We apply these methods, to an example of combinatorial optimisation in the form of a facilities location problem. Methods explored include the applications of different types of mixer (X, XY and a novel 3XY mixer) within QAOA as well as the effects of many settings for important meta parameters, which are often not focused on in research papers. Similarly, we explore alternative parameter settings in the context of quantum annealing. Our research confirms the broad consensus that quantum gate hardware will need to be much more capable than is available currently in terms of scale and fidelity to be able to address such problems at a commercially valuable level. Quantum annealing is closer to offering quantum advantage but will also need to achieve a significant step up in scale and connectivity to address optimisation problems where classical solutions are sub-optimal.