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This study corrects a hidden variables model that reproduces quantum correlations, simplifying it and demonstrating its effectiveness with simulations. The findings clarify how data-rejection in particle detection influences observed correlations.

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Area of Science:

  • Quantum mechanics
  • Hidden variables theory
  • Statistical modeling

Background:

  • Local hidden variables models attempt to explain quantum correlations without non-locality.
  • Pearle's (1970) model used data-rejection to reproduce singlet correlations.
  • Data-rejection in Pearle's model depends on hidden variables and measurement settings.

Purpose of the Study:

  • To correct a normalization error in Pearle's (1970) hidden variables model.
  • To demonstrate the inherent simplicity of Pearle's model.
  • To provide a clearer understanding of data-rejection mechanisms in quantum correlation models.

Main Methods:

  • Correction of normalization errors in Pearle's model formulas.
  • Conceptual simplification and analysis of the model's structure.
  • Visualization of the model's behavior.
  • Simulation experiment using the R statistical programming language.

Main Results:

  • A corrected and simplified version of Pearle's local hidden variables model was presented.
  • The model accurately reproduces quantum singlet correlations through a data-rejection mechanism.
  • The study highlights the dependence of detected particle subsamples on hidden variables and measurement settings.

Conclusions:

  • Pearle's model, once corrected, offers a simpler explanation for quantum correlations than previously suggested.
  • Data-rejection is a crucial factor in understanding how local hidden variables can mimic quantum phenomena.
  • The findings provide a foundation for further research into local hidden variables models and their implications.