mlsanityData to 7 Oct 2026

Optimizing Tensor Network Contraction Using Reinforcement Learning

arXiv 2204.09052 ↗First posted 18 Apr 2022quant-ph cs.LGcited by 5

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Abstract (arXiv)

Quantum Computing (QC) stands to revolutionize computing, but is currently still limited. To develop and test quantum algorithms today, quantum circuits are often simulated on classical computers. Simulating a complex quantum circuit requires computing the contraction of a large network of tensors. The order (path) of contraction can have a drastic effect on the computing cost, but finding an efficient order is a challenging combinatorial optimization problem. We propose a Reinforcement Learning (RL) approach combined with Graph Neural Networks (GNN) to address the contraction ordering problem. The problem is extremely challenging due to the huge search space, the heavy-tailed reward distribution, and the challenging credit assignment. We show how a carefully implemented RL-agent that uses a GNN as the basic policy construct can address these challenges and obtain significant improvements over state-of-the-art techniques in three varieties of circuits, including the largest scale networks used in contemporary QC.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 22 of its 46 references that are in the map. First posted on arXiv 18 Apr 2022.

Builds on

22 of 46 references.

References · 22

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10 more
  1. 2019Approximation Ratios of Graph Neural Networks for Combinatorial Problemscited by 68
  2. 2019Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use casecited by 53
  3. 2016Supervised Learning with Quantum-Inspired Tensor Networkscited by 35
  4. 2017Solving a New 3D Bin Packing Problem with Deep Reinforcement Learning Methodcited by 30
  5. 2020Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networkscited by 30
  6. 2020Quantum Computing: A Taxonomy, Systematic Review and Future Directionscited by 16
  7. 2021No-Regret Reinforcement Learning with Heavy-Tailed Rewardscited by 13
  8. 2020A Survey on Reinforcement Learning for Combinatorial Optimizationcited by 10
  9. 2015k-way Hypergraph Partitioning via n-Level Recursive Bisectioncited by 7
  10. 2020Algorithms for Tensor Network Contraction Orderingcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 2; 2026: 1.

5 papers.

  1. 2025Domain-Aware Tensor Network Structure Searchcited by 5
  2. 2023Intrinsically motivated graph exploration using network theories of human curiositycited by 3
  3. 2022Sampling-Based Decomposition Algorithms for Arbitrary Tensor Networkscited by 2
  4. 2025Combining Local Symmetry Exploitation and Reinforcement Learning for Optimised Probabilistic Inference -- A Work In Progresscited by 0
  5. 2026Algorithmic algorithm development with LLMs: A Case Study on LLM-Usage for Contraction Order Optimization in Tensor Networkscited by 0