Lifetime-based Optimization for Simulating Quantum Circuits on a New Sunway Supercomputer

TL;DR

This paper introduces lifetime-based optimization techniques for tensor network contraction to efficiently simulate quantum circuits on the Sunway supercomputer, significantly reducing simulation time and improving performance.

Contribution

It proposes novel lifetime-based methods and an adaptive contraction path refiner that reduce slicing overhead and enhance simulation efficiency on Sunway architecture.

Findings

Slicing overhead is reduced compared to cotengra.

Simulation time for Sycamore quantum processor is decreased to 96.1s.

Achieved over 5 times performance improvement from previous work.

Abstract

High-performance classical simulator for quantum circuits, in particular the tensor network contraction algorithm, has become an important tool for the validation of noisy quantum computing. In order to address the memory limitations, the slicing technique is used to reduce the tensor dimensions, but it could also lead to additional computation overhead that greatly slows down the overall performance. This paper proposes novel lifetime-based methods to reduce the slicing overhead and improve the computing efficiency, including an interpretation method to deal with slicing overhead, an in-place slicing strategy to find the smallest slicing set and an adaptive tensor network contraction path refiner customized for Sunway architecture. Experiments show that in most cases the slicing overhead with our in-place slicing strategy would be less than the cotengra, which is the most used graph path optimization software at present. Finally, the resulting simulation time is reduced to 96.1s for the Sycamore quantum processor RQC, with a sustainable single-precision performance of 308.6Pflops using over 41M cores to generate 1M correlated samples, which is more than 5 times performance improvement compared to 60.4 Pflops in 2021 Gordon Bell Prize work.