Title: Achieving Energetic Superiority Through System-Level Quantum Circuit Simulation Show Chinese title

Title: 通过系统级量子电路仿真实现能量优势

Abstract: Quantum Computational Superiority boasts rapid computation and high energy efficiency. Despite recent advances in classical algorithms aimed at refuting the milestone claim of Google's sycamore, challenges remain in generating uncorrelated samples of random quantum circuits. In this paper, we present a groundbreaking large-scale system technology that leverages optimization on global, node, and device levels to achieve unprecedented scalability for tensor networks. This enables the handling of large-scale tensor networks with memory capacities reaching tens of terabytes, surpassing memory space constraints on a single node. Our techniques enable accommodating large-scale tensor networks with up to tens of terabytes of memory, reaching up to 2304 GPUs with a peak computing power of 561 PFLOPS half-precision. Notably, we have achieved a time-to-solution of 14.22 seconds with energy consumption of 2.39 kWh which achieved fidelity of 0.002 and our most remarkable result is a time-to-solution of 17.18 seconds, with energy consumption of only 0.29 kWh which achieved a XEB of 0.002 after post-processing, outperforming Google's quantum processor Sycamore in both speed and energy efficiency, which recorded 600 seconds and 4.3 kWh, respectively. Show Chinese abstract

Abstract: 量子计算优势具有快速计算和高能效的特点。 尽管在经典算法方面取得了进展，旨在反驳谷歌Sycamore的里程碑声明，但生成随机量子电路的不相关样本仍然存在挑战。 在本文中，我们提出了一种突破性的大规模系统技术，该技术利用全局、节点和设备级别的优化，实现了张量网络前所未有的可扩展性。 这使得能够处理内存容量达到数十TB的大规模张量网络，超越单个节点的内存空间限制。 我们的技术能够容纳多达数十TB内存的大规模张量网络，最多可使用2304块GPU，峰值计算能力为561 PFLOPS半精度。 值得注意的是，我们在能耗为2.39 kWh的情况下实现了14.22秒的求解时间，达到了0.002的保真度，我们最显著的结果是在后处理后仅消耗0.29 kWh能耗的情况下实现了17.18秒的求解时间，达到了XEB值为0.002，其速度和能效均超过了谷歌的量子处理器Sycamore，后者分别记录了600秒和4.3 kWh。