Department of Computer Science, University of Chicago, Chicago, USA.
Department of Computer Science, Princeton University, Princeton, USA.
Nature. 2017 Sep 13;549(7671):180-187. doi: 10.1038/nature23459.
Quantum computing sits at an important inflection point. For years, high-level algorithms for quantum computers have shown considerable promise, and recent advances in quantum device fabrication offer hope of utility. A gap still exists, however, between the hardware size and reliability requirements of quantum computing algorithms and the physical machines foreseen within the next ten years. To bridge this gap, quantum computers require appropriate software to translate and optimize applications (toolflows) and abstraction layers. Given the stringent resource constraints in quantum computing, information passed between layers of software and implementations will differ markedly from in classical computing. Quantum toolflows must expose more physical details between layers, so the challenge is to find abstractions that expose key details while hiding enough complexity.
量子计算正处于一个重要的转折点。多年来，量子计算机的高级算法显示出了相当大的前景，而最近在量子器件制造方面的进展也为其实用性带来了希望。然而，量子计算算法的硬件规模和可靠性要求与未来十年内预期的物理机器之间仍然存在差距。为了弥合这一差距，量子计算机需要适当的软件来转换和优化应用程序（工具流）和抽象层。考虑到量子计算中资源的严格限制，软件和实现之间的信息传递将与经典计算显著不同。量子工具流必须在层之间暴露更多的物理细节，因此挑战在于找到能够暴露关键细节而又隐藏足够复杂性的抽象。
