Summary of Charme: a Chain-based Reinforcement Learning Approach For the Minor Embedding Problem, by Hoang M. Ngo et al.
CHARME: A chain-based reinforcement learning approach for the minor embedding problem
by Hoang M. Ngo, Nguyen H K. Do, Minh N. Vu, Tamer Kahveci, My T. Thai
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach called CHARME that uses Reinforcement Learning (RL) techniques to address the minor embedding problem in Quantum Annealing (QA). The minor embedding problem involves representing logical graphs as inputs for QA’s quantum unit processing, which has limited connectivity. Existing methods suffer from scalability issues with larger problem sizes. CHARME includes a Graph Neural Network (GNN) architecture for policy modeling, a state transition algorithm ensuring solution validity, and an order exploration strategy for effective training. The proposed method is compared to existing approaches like Minorminer, ATOM, and OCT-based methods on synthetic and real-world instances, demonstrating superior solutions in several cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to help computers solve complex problems using quantum annealing. Quantum annealing is a powerful tool that can quickly find the best solution among many options. However, it needs special help to turn problem descriptions into inputs that the computer can understand. The authors created a new approach called CHARME that uses machine learning to solve this problem. They compared their method to other approaches and found that it works better in some cases. |
Keywords
» Artificial intelligence » Embedding » Gnn » Graph neural network » Machine learning » Reinforcement learning
