In the span of four decades, quantum computation has evolved from an intellectual curiosity to a potentially realizable technology. Today, small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. Nevertheless, there are significant outstanding challenges in quantum hardware, fabrication, software architecture, and algorithms on the path towards a full-stack scalable quantum computing technology. Here, we provide a comprehensive review of these scaling challenges. We show how to facilitate scaling by adopting existing semiconductor technology to build much higher-quality qubits, employing systems engineering approaches, and performing distributed heterogeneous quantum-classical computing. We provide a detailed resource and sensitivity analysis for quantum applications on surface-code error-corrected quantum computers given current, target, and desired hardware specifications based on superconducting qubits, accounting for a realistic distribution of errors. We provide comprehensive resource estimates for several utility-scale applications including quantum chemistry calculations, catalyst design, NMR spectroscopy, and Fermi-Hubbard simulation. We show that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration. Furthermore, we introduce high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical optimization, machine learning, and quantum simulation tasks in a cost-effective manner.

翻译：在过去的四十年间，量子计算已从一项理论探索演变为具有现实可行性的技术。如今，在数百个物理量子比特上实现量子算法原型的初步演示已成为可能。然而，在实现全栈可扩展量子计算技术的道路上，量子硬件、制造工艺、软件架构及算法等方面仍存在重大挑战。本文系统综述了这些规模化挑战。我们展示了如何通过采用现有半导体技术构建更高品质的量子比特、运用系统工程方法、以及实施分布式异构量子-经典计算来促进规模化发展。基于超导量子比特技术，我们结合当前、目标及理想的硬件规格，考虑实际误差分布，对表面码纠错量子计算机上的量子应用进行了详细的资源与灵敏度分析。我们针对多个实用级应用提供了全面的资源估算，包括量子化学计算、催化剂设计、核磁共振波谱分析以及费米-哈伯德模型模拟。研究表明，通过硬件改进与量子-高性能计算的深度融合，可实现数个数量级的性能提升。此外，我们提出了面向量子概率计算的高性能架构，通过定制化加速器以经济高效的方式应对当前工业规模的经典优化、机器学习及量子模拟任务。