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Searching for spin glass ground states through deep reinforcement learning
Changjun Fan1, Mutian Shen2, Zohar Nussinov2,3,4
1College of Systems Engineering, National University of Defense Technology, 410073, Changsha, China.
Nature Communications
|February 9, 2023
Summary
A new deep reinforcement learning framework, DIRAC, efficiently finds ground states for spin glasses, aiding disordered magnet research and complex optimization problems. It scales effectively and enhances existing methods.
Area of Science:
- Statistical Physics
- Artificial Intelligence
- Combinatorial Optimization
Background:
- Spin glasses are disordered magnetic systems with conflicting interactions, posing challenges in understanding their nature and solving complex optimization problems.
- Finding the ground states of spin glasses is crucial for both fundamental physics research and practical applications in various disciplines.
- Existing algorithms lack the desired combination of high accuracy and efficiency for spin glass ground state determination.
Purpose of the Study:
- To introduce DIRAC, a novel deep reinforcement learning framework for efficiently determining spin glass ground states.
- To demonstrate DIRAC's ability to train on small instances and generalize to arbitrarily large ones.
- To explore the connection between physics and artificial intelligence through novel techniques like gauge transformations.
Main Methods:
- Development of DIRAC, a deep reinforcement learning framework.
- Training DIRAC on small-scale spin glass instances.
- Application of DIRAC to 2D, 3D, and 4D Edwards-Anderson spin glass models.
- Enhancement of thermal annealing methods using DIRAC.
- Utilizing gauge transformation techniques within the AI framework.
Main Results:
- DIRAC demonstrates superior scalability compared to existing methods.
- The framework achieves high accuracy and efficiency in finding spin glass ground states.
- DIRAC's performance is validated on various dimensional spin glass instances.
- The method can be integrated to improve any thermal annealing technique.
Conclusions:
- DIRAC offers a highly accurate and efficient solution for spin glass ground state determination.
- The framework advances the understanding of low-temperature spin glass phases, a key challenge in statistical physics.
- DIRAC establishes a significant link between artificial intelligence and physics, opening new avenues for reinforcement learning in complex problem-solving.
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