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SW-TNC : Reaching the Most Complex Random Quantum Circuit via Tensor Network Contraction
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SW-TNC : Reaching the Most Complex Random Quantum Circuit via Tensor Network Contraction
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Classical simulation is essential in quantum algorithm development and quantum device verification. With the increasing complexity and diversity of quantum circuit structures, existing classical simulation algorithms need to be improved and extended. In this work, we propose novel strategies for tensor network contraction based simulator on Sunway architecture. Our approach addresses three main aspects: complexity, computational paradigms and fine-grained optimization. Data reuse schemes are designed to reduce floating-point operations, and memory organization techniques are employed to eliminate slicing overhead while maintaining parallelism. Step fusion strategy is extended by multi-core cooperation to improve the data locality and computation intensity. Fine-grained optimizations, such as in-kernel vectorized permutations, and split-K operators, are developed as well to address the challenges in new hotspot distribution and topological structure. These innovations can accelerate the simulation of the Zuchongzhi-60-24 by more than 10 times, using more than 1024 Sunway nodes (399,360 cores). Our work demonstrates the potential for enabling efficient classical simulation of increasingly complex quantum circuits.
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Cited by 1 Pith paper
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Parallelizing Large-Scale Tensor Network Contraction on Multiple GPUs
A communication-aware multi-GPU distribution approach for tensor network contraction reports 7-173x extra speedup over slicing on 8 H100 GPUs and 42x to 67,869x on 1024 GPUs.
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