Exploring the Phenomena of a Quantum Eraser in Young’s Double Slit Experiment
Bibliographic record
Abstract
This experiment explores the quantum phenomenon known as the Quantum Eraser, using a variation of Young’s Double Slit experiment. Young’s Double Slit experiment demonstrates that light acts as a wave by creating an interference pattern when diffracted through two slits. If one measures which of the two slits the photons pass through, then the interference pattern is replaced by a single bright spot, as would be expected for particle-like behaviour. The “Quantum Eraser” eliminates the measurement on the photons, thereby reintroducing the interference pattern observed in Young’s original experiment. The experiment’s first stage saw Young’s Double Slit experiment recreated and an interference pattern was observed. Upon adding two orthogonally polarized filters, the photon’s path was measured, and the interference pattern was removed. By then adding a third filter which was polarized 45O relative to both other polarisers, the interference pattern was somewhat restored. For each experiment, the heights of the peaks in the interference patterns were compared to each other to examine the quality of the reproduced interference pattern based on the original double slit interference pattern. This comparison gave a quantitative result that demonstrated that the Quantum Eraser was able to restore the interference pattern to within 5 standard errors, thereby exemplifying the effect that changing the measurement conditions affects the final measurement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".