Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions
Bibliographic record
Abstract
This data repository contains codes and data to replicate all the figures and experimental results in the main text and the supplementary material of the paper "Linear-depth quantum circuits for loading Fourier approximations of arbitrary functions". In particular, it has codes to run the simulations of the Fourier Series Loader (FSL) using Qiskit and codes to analyze the experimental results obtained from Quantinuum System Model H1 quantum computers. Moreover, for each experiment performed on Quantinuum System Model H1, this repository contains the qasm file that was used to run the experiment and a data file containing the results of the experiment. The contents of this data repository are as follows: There are seven notebooks, each corresponding to one of the figures from the paper. These notebooks are named after the figure that they correspond to. For example, the notebook 'Figure_2.ipynb' contains code to reproduce all of the subfigures in Fig. (2) of the paper. There are also two code files, 'uniformly_controlled_rotations.py' and 'supplementary.py', that are needed to perform the Qiskit simulations in notebooks 'Figure_2.ipynb' and 'Figure_3.ipynb'. All the experimental data is in the folder 'experimental_data' and all the qasm files are in the folder 'qasm_files'. Each of these folders contains a README.txt file with further details about the contents of these folders. Finally, all the required packages to run the code in this repository are listed in the file 'requirements.txt". Since the motivation for this repository was reproducibility, it only has the necessary codes to reproduce the simulation and experimental results presented in our paper. It, however, does not contain a well-documented implementation of the FSL method. We refer interested readers to the GitHub repository (https://github.com/mcmahon-lab/Fourier-Series-Loader), which has step-by-step tutorials for using the FSL method in Qiskit for various types of functions. Interested users can adapt these examples for their own use of the FSL method.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.284 | 0.122 |
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".