Searching for a pixel's position in a grayscale quantum image with Grover's algorithm
Bibliographic record
Abstract
This work proposes a circuit implementation of the encoding circuit for an N × N grayscale-based Flexible Representation of Quantum Images (FRQI). The implementation is tested on the Qiskit simulator before being executed on real IBM Quantum hardware. The encoded FRQI, with a searched pixel encoded by a single qubit, is considered an unsorted database with a single table, where the key of the table represents the pixel’s position (x, y). The other columns represent the grayscale level at this position and the searched grayscale level, which are encoded by rotation angles and implemented using several multi-controlled rotation gates along the y-axis. Subsequently, Grover ’s algorithm is used to retrieve the position from the FRQI after performing a comparison with the given grayscale level of an individual pixel. The physical constraints associated with the IBM quantum device used are discussed, and the limitations of Grover ’s algorithm for searching the pixel are addressed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".