Analog-Quantum Feature Mapping for Machine-Learning Applications
Bibliographic record
Abstract
Quantum information processing is likely to have a far-reaching impact in the field of artificial intelligence. Noisy, intermediate-scale quantum devices provide a platform for exploring the possibility of attaining a quantum advantage through hybrid quantum-classical machine-learning algorithms. One example of such a hybrid algorithm is ``quantum kitchen sinks,'' which builds upon a classical algorithm known as ``random kitchen sinks'' to leverage a gate model quantum computer for machine-learning applications. We propose an alternative algorithm called ``analog-quantum kitchen sinks'' (AQKSs), which employs an analog-quantum computer for mapping data features into new features in a nonlinear manner. The new features can then be used by a classical algorithm to perform machine-learning tasks. We show the effectiveness of our algorithm for performing binary classification on both a synthetic dataset and a real-world dataset by simulating the operations of a quantum annealer. We demonstrate that the AQKS algorithm reduces the classification error of a linear classifier from $50\mathrm{%}$ to $0.6\mathrm{%}$ for the synthetic dataset and from $4.4\mathrm{%}$ to $1.6\mathrm{%}$ for the other dataset. Our proposed AQKS algorithm presents the possibility to use current quantum annealers for solving practical machine-learning problems.
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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 | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".