ArcTN: Efficient Tensor-Network Contraction Planning for Quantum Circuit Simulation
Abstract
Tensor-network simulation of quantum circuits depends on contraction orders with low computational cost and small intermediate tensors, found without excessive planning time. We present ArcTN, a contraction-order optimization framework with two search presets. Light keeps planning overhead low, and Heavy spends more effort on refinement. Both combine randomized-greedy construction, subtree reconfiguration, and parallel tempering with stage-specific stopping rules. ArcTN also provides Fixed and Dynamic slicing to meet tensor-size limits and executes the returned orders. On the 30 QC32 quantum-circuit networks completed by all eight compared configurations, Light reduces FLOPs by 61.32% and plans 1.62× faster than Cotengra Auto/CMA-ES, and Heavy reduces FLOPs by 43.83% and plans 7.21× faster than Cotengra AutoHQ/Optuna. Heavy also needs 11.85% fewer FLOPs than OMECO TreeSA-8 and plans 1.33× faster. Dynamic slicing meets the size limits in all 90 runs on 18 scalar-output networks and reduces total sliced FLOPs by 31.99% relative to Fixed slicing, at the cost of longer planning. With a common NumPy executor, Heavy has the lowest combined planning and execution time among five configurations on the runs that all of them completed. On 672 networks built from 320 MQT Bench circuits, Heavy obtains the lowest geometric-mean FLOPs on the 142 large networks while planning 3.1× and 31× faster than Cotengra AutoHQ and Hyper128.
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BibTeX
@misc{liu2026arctn,
title = {{ArcTN: Efficient Tensor-Network Contraction Planning for Quantum Circuit Simulation}},
author = {Zhengyi Liu and Li Liu and Songtao Liu and Xianglong Liu and Xiangzhen Zhou and Mingyu Huang and Dingchao Gao and Shenggang Ying and Bolong Chen and Ji Guan},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.31554},
eprint = {2610.31554},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.31554},
}
RIS
TY - UNPB
TI - ArcTN: Efficient Tensor-Network Contraction Planning for Quantum Circuit Simulation
AU - Zhengyi Liu
AU - Li Liu
AU - Songtao Liu
AU - Xianglong Liu
AU - Xiangzhen Zhou
AU - Mingyu Huang
AU - Dingchao Gao
AU - Shenggang Ying
AU - Bolong Chen
AU - Ji Guan
PY - 2026
DA - 2026/10/23
PB - arXiv
DO - 10.48550/arxiv.2610.31554
UR - https://arxiv.org/abs/2610.31554
AB - Tensor-network simulation of quantum circuits depends on contraction orders with low computational cost and small intermediate tensors, found without excessive planning time. We present ArcTN, a contraction-order optimization framework with two search presets. Light keeps planning overhead low, and Heavy spends more effort on refinement. Both combine randomized-greedy construction, subtree reconfiguration, and parallel tempering with stage-specific stopping rules. ArcTN also provides Fixed and Dynamic slicing to meet tensor-size limits and executes the returned orders. On the 30 QC32 quantum-circuit networks completed by all eight compared configurations, Light reduces FLOPs by 61.32% and plans 1.62× faster than Cotengra Auto/CMA-ES, and Heavy reduces FLOPs by 43.83% and plans 7.21× faster than Cotengra AutoHQ/Optuna. Heavy also needs 11.85% fewer FLOPs than OMECO TreeSA-8 and plans 1.33× faster. Dynamic slicing meets the size limits in all 90 runs on 18 scalar-output networks and reduces total sliced FLOPs by 31.99% relative to Fixed slicing, at the cost of longer planning. With a common NumPy executor, Heavy has the lowest combined planning and execution time among five configurations on the runs that all of them completed. On 672 networks built from 320 MQT Bench circuits, Heavy obtains the lowest geometric-mean FLOPs on the 142 large networks while planning 3.1× and 31× faster than Cotengra AutoHQ and Hyper128.
ER -