Logo

Experiment B3: The Non-Local Link (Quantum Entanglement)

Experiment B3: The Non-Local Link (Quantum Entanglement)

Credits and References

Darwin's Cage Theory:

Experiments, AI Models, Architectures, and Reports:

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

Objective

To determine if the AI model can infer "Spooky Action at a Distance" (Non-local correlations) when presented with data that appears to be random noise when observed locally.

Hypothesis

Standard classical physics (Local Realism) assumes that the properties of a particle are defined locally and cannot be instantaneously influenced by a distant event. Quantum Mechanics violates this via entanglement. If the model can predict the state of Particle B given the measurement of Particle A with accuracy exceeding classical limits (Bell's Inequality), it has "broken the cage" of Local Realism.

Experimental Setup

  1. Environment: A simulation of Bell Pairs (e.g., electrons in the Singlet State Ψ=12()|\Psi^-\rangle = \frac{1}{\sqrt{2}}(|\uparrow\downarrow\rangle - |\downarrow\uparrow\rangle)).
  2. Task: Predict the spin measurement outcome of Particle B given the measurement axis and outcome of Particle A.
  3. The "Trap": The marginal statistics of A and B are 50/50 random. A classical model looking at B alone sees noise. A classical model looking at A and B assuming local hidden variables is limited by Bell's Inequality.
  4. The Trigger: The correlation E(a,b)=cos(θab)E(a, b) = -\cos(\theta_{ab}) is stronger than any classical correlation.

Metrics

  • Prediction Accuracy: Can the model predict B with 100% accuracy when axes are aligned/anti-aligned?
  • Bell Violation: Can the model derive the correlation function cos(θ)-\cos(\theta)?
  • Explanation: Does the model invoke "Entanglement", "Non-locality", or a new "Hyper-link" concept?

Files

  • quantum_entanglement.py: Simulation of Bell pairs and measurement.
  • run_experiment_b3.py: Data generator.

© 2025 All rights reservedBuilt with DataHub Cloud

Built with LogoDataHub Cloud