  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.
