Quantum Algorithm for Handling Large-Scale Image Data in Quantum Random Access Memory
Bibliographic record
Abstract
The use of quantum computers to transport and process large-scale, high-resolution, real-time captured images highlights the need for efficient quantum storage methods, particularly in fields such as satellite imaging and astronomy. This paper presents a novel algorithm for integrating real-time captured quantum images that are captured using multi-imaging sources and storing these images to Quantum Random Access Memory (QRAM), utilizing an efficient fixed architecture QRAM in time cost$O(1)$. Then, the proposed algorithm reads these images from QRAM in time cost$O(1)$to integrate these images and reconstruct a single image from these images. This algorithm is realized experimentally using IMB's Aer simulator with two different image types and quantum representations. The first experiment is conducted on a one-channel (grayscale) image with pixel intensity values encoded as probability amplitudes. The second experiment is executed on a three-channel (RGB) image with pixel intensity values encoded in basis states. The results showcased the robustness and compatibility of the proposed algorithm with different quantum image formats.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".