Quantum Cage Experiment: Breaking the Darwinian Cage in Quantum Systems

Credits and References

Darwin's Cage Theory:

• Theory Creator: Gideon Samid
• Reference: Samid, G. (2025). Negotiating Darwin's Barrier: Evolution Limits Our View of Reality, AI Breaks Through. Applied Physics Research, 17(2), 102. https://doi.org/10.5539/apr.v17n2p102
• Publication: Applied Physics Research; Vol. 17, No. 2; 2025. ISSN 1916-9639 E-ISSN 1916-9647. Published by Canadian Center of Science and Education
• Available at: https://www.researchgate.net/publication/396377476_Negotiating_Darwin's_Barrier_Evolution_Limits_Our_View_of_Reality_AI_Breaks_Through

Experiments, AI Models, Architectures, and Reports:

• Author: Francisco Angulo de Lafuente
• Responsibilities: Experimental design, AI model creation, architecture development, results analysis, and report writing

Executive Summary

This experiment explored whether artificial intelligence models can develop representations that transcend classical human-interpretable variables when learning quantum dynamics. The results demonstrate that our model successfully "broke the cage" of classical variables, developing novel representations that are not correlated with traditional position and momentum concepts.

1. Experimental Overview

1.1 Objective

To determine if machine learning models can develop representations of quantum systems that are not bound by classical physical variables, thereby "breaking the Darwinian Cage" of human conceptual frameworks.

1.2 Methodology

• System: Quantum particle in a double-well potential
• Model: Neural network with complex number handling
• Training Data: 100 quantum state trajectories (64 points each)
• Training Epochs: 50
• Validation: 20% holdout set
• Comparative Baseline: Classical model using amplitude and phase decomposition

2. Key Findings

2.1 Cage Breaking Analysis

The near-zero correlations between principal components and classical variables indicate the model developed representations independent of human-interpretable concepts.

2.2 Model Performance

3. Technical Implementation

3.1 Model Architecture

• Input Layer: 128 neurons (64 complex values → 128 real values)
• Hidden Layers: 3 fully connected layers (256 neurons each)
• Activation: ReLU
• Output Layer: 128 neurons (64 complex values)
• Optimizer: Adam (lr=1e-3)
• Loss Function: MSE on real and imaginary components

3.2 Data Generation

• System: Double-well potential
• States: Superposition of Gaussian wavepackets
• Dynamics: Schrödinger equation evolution

4. Interpretation of Results

4.1 Cage Breaking Evidence

The model's internal representations show:

• No significant correlation with position or momentum
• Meaningful structure (22.28% variance in first 2 PCs)
• Successful learning of quantum dynamics

This suggests the model developed a novel, non-classical understanding of the quantum system.

4.2 Performance Analysis

• The quantum model achieved excellent training and validation performance
• The classical model showed good amplitude prediction but struggled with phase dynamics
• The small train-val loss gap indicates good generalization

5. Conclusions

1. Cage Broken: The model developed representations that are not correlated with classical variables, suggesting it "broke the cage" of human conceptual frameworks.

2. Novel Representations: The success of the model indicates that AI can discover alternative ways to understand quantum phenomena beyond classical physics concepts.

3. Implications: This has significant implications for using AI in quantum physics research, as it suggests AI might discover fundamentally new ways to understand and work with quantum systems.

6. Recommendations

1. Further Research:

   • Investigate the nature of the learned representations
   • Test with more complex quantum systems
   • Explore if these representations provide computational advantages

2. Technical Improvements:

   • Scale up the model and training data
   • Investigate different neural network architectures
   • Apply interpretability techniques to understand the learned representations

3. Applications:

   • Quantum control
   • Materials discovery
   • Quantum algorithm development

7. Data Availability

All code, data, and results are available in the experiment directory:

• quantum_cage.py: Core model implementation
• benchmark_quantum_cage.py: Experiment execution
• results/: Detailed results and visualizations

8. Acknowledgements

This research was conducted using the Darwin Cage experimental framework. The authors acknowledge the developers of PyTorch and other open-source libraries that made this work possible.

Date: November 27, 2025
Author: Francisco Angulo de Lafuente
Theoretical Framework: Gideon Samid (Darwin's Cage Theory)