Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation
Classical simulation remains essential for developing and validating quantum algorithms, but its cost grows rapidly with circuit size. Tensor-network contraction can reduce this cost by exploiting circuit structure, although its efficiency depends strongly on the chosen contraction plan. On GPUs, plans with similar theoretical complexity may perform very differently because execution also depends on parallelism, reduction structure, memory traffic, and contraction geometry. We present a learning-to-rank framework for selecting efficient contraction plans before executing them. Each plan is represented by structural features derived directly from its sequence of pairwise contractions, and ...
| Authors | Alfred M. Pastor, Maribel Castillo, Jose M. Badia |
|---|---|
| Primary category | cs.LG |
| Published | 6 August 2026 |
| Last updated at source | 2 October 2026 |
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