# Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation

arXiv: https://arxiv.org/abs/2608.05819 · HTML: https://mlsanity.com/paper/2608.05819/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 60 references that are in the map.
- First posted on arXiv 6 Aug 2026.

## Builds on (most cited first, 17 of 17)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning to learn with quantum neural networks via classical neural networks](https://mlsanity.com/paper/1907.05415/) (2019)
- [Learning to Decode the Surface Code with a Recurrent, Transformer-Based Neural Network](https://mlsanity.com/paper/2310.05900/) (2023)
- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [Computing Tree Decompositions with FlowCutter: PACE 2017 Submission](https://mlsanity.com/paper/1709.08949/) (2017)
- [Efficient and Effective Tree-based and Neural Learning to Rank](https://mlsanity.com/paper/2305.08680/) (2023)
- [A Survey on E-Commerce Learning to Rank](https://mlsanity.com/paper/2412.03581/) (2024)
- [Graph Bisection with Pareto-Optimization](https://mlsanity.com/paper/1504.03812/) (2015)
- [Learning to rank quantum circuits for hardware-optimized performance enhancement](https://mlsanity.com/paper/2404.06535/) (2024)
- [High-Performance Tensor Contraction without Transposition](https://mlsanity.com/paper/1607.00291/) (2016)
- [Parameterization of tensor network contraction](https://mlsanity.com/paper/1906.00013/) (2019)
- [Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs](https://mlsanity.com/paper/2310.03978/) (2023)
- [Benchmarking treewidth as a practical component of tensor-network--based quantum simulation](https://mlsanity.com/paper/1807.04599/) (2018)
- [Feature Interactions in XGBoost](https://mlsanity.com/paper/2007.05758/) (2020)
- [Recent Advances in the Foundations and Applications of Unbiased Learning to Rank](https://mlsanity.com/paper/2305.02914/) (2023)
- [Unified Learning-to-Rank for Multi-Channel Retrieval in Large-Scale E-Commerce Search](https://mlsanity.com/paper/2602.23530/) (2026)

## Abstract (arXiv)

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 gradient-boosted rankers are trained from GPU measurements using listwise and pairwise objectives. We evaluate the resulting models on diverse circuit families, using separate in-distribution and circuit-family-shift test sets, and compare them with random and MinFill-based baselines. The learned rankers generally identify better plans, with the listwise model providing the strongest overall decision quality. We also study backend shift by comparing empirical plan orderings on two GPU architectures and evaluating the source-trained models on the second device without retraining. The rankings remain substantially, though not perfectly, stable across GPUs, and the models retain useful decision quality. These results support Learning to Rank as a practical way to reduce contraction-plan search, while showing that performance remains partly backend dependent.