Quantum Field Lens Coding Software for System State Simulation, Strong Prediction and Game Application
Bibliographic record
Abstract
This software program (QFLCS) analyzes the measurement outcome probability (P) data from datasets generated by Quantum Double-field (QDF) Circuits. The datasets are compared between ES and GS states as a P indicator generated for measurement samples. Small dataset samples denote: a. A particle pair’s energy state in a QDF, superposing between QDF points (sublevels of a GS, or see Table 2 in Sec. 3 of the published article), b. a single field (SF or particle state), an ES relative to a GS from (a.), prior to its transform into a QDF, c. the expected transformation of fields (ES ←→GS) and their ⟨M(P, ψ_ij)⟩, as in Sec. 3 of the published article. The file structure here is a mirror of the Mendeley repository file structure of v3+ at https://data.mendeley.com/datasets/gf2s8jkdjf/3, but with a smaller file size for efficient download and use of the QFLCA project's code and documentation (website). Certain small updates have been made in the main python file uploaded here on Code Ocean for minor debugging purposes. * The main file is which imports and executes the or QDF-LCode_IBMQ-2024.py code for the simulation under Win OS or Linux OS. * We recommend downloading the entire directory according to the folder structure and run QAI-LCode_QFLCC.py in VSC with python latest packages installed for windows OS (the QDF game is developed for Windows OS, yet parts of the code for sound and display can be rewritten for Linux OS), e.g. "winsound" package as a compatible option. Other packages are needed to be installed, or code rewritten for "sound" and "display" compatibility under other operating systems. * The QAI-LCode_QFLCC.py file has a Pygame GUI and other packages suited for local machine runs, rather than running this file on the Code Ocean platform which could take hours to compile and run a compatible program/game with packages. However, the QDF-LCode_IBMQ-2024-codable.py can be run here as the core of the simulation program simulating the QDF circuit. A short presentation explaining these points are given in the directory as the "QAI-COcean-Demo.mp4" file. * The User and Developer’s documentation/manual/demo is found under the directory, as and contents. * In each folder, , , and under , Tips.txt and/or ReadMe.txt files exist to explain the contents of that directory. Also, under directory, a ReadMe file exists explaining the manual computation and presentation parts of the project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.015 |
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".