Large-Scale Simulation of Quantum Circuits via Tensor Network Contraction
ORAL
Abstract
In this work, we present a parallel tensor network contraction algorithm for quantum circuit simulations. The key ingredient for the efficient parallelization, called index slicing, reduces the space complexity and enables embarrassing parallelism with little total overhead. To benchmark the performance of the algorithm, we experiment on the Google Sycamore Random Circuit Sampling task performed by Arute et al. (Nature, 574, 505--510 (2019)), which was initially estimated to require Summit, the world's most powerful supercomputer today, approximately 10,000 years. We estimate that our simulator can perform this task in less than 20 days on a Summit-comparable cluster. These estimates are based on explicit simulations of parallel subtasks, and leave no room for hidden costs. We further apply our algorithm to simulate quantum error correction under more realistic error models, as well as near-term quantum algorithms. An implementation of the algorithm is now open- sourced as Alibaba Cloud Quantum Development Platform.
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Presenters
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Cupjin Huang
- Alibaba Quantum Laboratory, Alibaba Group
- Alibaba Group, Alibaba Quantum Laboratory