Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schr\"odinger Equation
📰 ArXiv cs.AI
Physics-Informed Evolution is a framework that combines evolutionary algorithms with physical laws to solve quantum control problems involving the Schr"odinger Equation
Action Steps
- Combine physical laws with evolutionary algorithms to create a physics-informed evolutionary framework
- Apply this framework to solve quantum control problems involving the Schr"odinger Equation
- Use the framework to optimize objective functions by simulating natural selection processes
- Evaluate the performance of the framework in solving quantum control problems
Who Needs to Know This
Researchers and engineers working on quantum control problems, particularly those with a background in physics and machine learning, can benefit from this framework as it enhances efficiency and physical consistency of solutions
Key Insight
💡 Embedding physical laws into evolutionary algorithms can enhance efficiency and physical consistency of solutions
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🔍 Physics-Informed Evolution: a new framework for solving quantum control problems with evolutionary algorithms #quantumcontrol #physicsinformed
Key Takeaways
Physics-Informed Evolution is a framework that combines evolutionary algorithms with physical laws to solve quantum control problems involving the Schr"odinger Equation
Full Article
Title: Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schr\"odinger Equation
Abstract:
arXiv:2502.05228v3 Announce Type: replace-cross Abstract: Physics-informed Neural Networks (PINNs) show that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. Similar to optimizing loss functions in machine learning, evolutionary algorithms iteratively optimize objective functions by simulating natural selection processes. Inspired by this principle, we ask a natural question: can physical i
Abstract:
arXiv:2502.05228v3 Announce Type: replace-cross Abstract: Physics-informed Neural Networks (PINNs) show that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. Similar to optimizing loss functions in machine learning, evolutionary algorithms iteratively optimize objective functions by simulating natural selection processes. Inspired by this principle, we ask a natural question: can physical i
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