Achieving Energetic Superiority Through System-Level Quantum Circuit Simulation

TL;DR

This paper demonstrates a large-scale quantum circuit simulation system that surpasses Google's Sycamore in speed and energy efficiency, enabling the handling of massive tensor networks with unprecedented scalability.

Contribution

The authors introduce a novel system-level optimization approach for tensor network simulation, achieving scalability to tens of terabytes and utilizing up to 2304 GPUs for quantum circuit simulation.

Findings

Achieved a time-to-solution of 14.22 seconds with 2.39 kWh energy consumption.

Reached a time-to-solution of 17.18 seconds with 0.29 kWh energy, outperforming Google's Sycamore.

Simulated large-scale tensor networks with up to tens of terabytes of memory.

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.