  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.
