On scalable quantum-inspired distributed machine learning
Bibliographic record
Abstract
Recently a new approach to train machine learning models, such as neural networks, has been introduced. This method is based on the simulation of a type of quantum systems, in particular systems of one-dimensional electrons coupled by a force, which offers important advantages. First, by exploiting simulations of the quantum tunnelling effect, a different, and faster, convergence in the training phase can be obtained. Secondly, it does not require the computation of any kind of gradient (in other words, it can be considered a derivative-free or gradient-free optimizer) thus allowing the training of complex models impossible to train otherwise, for instance with the gradient descent method, or variations of it. Finally, it allows the use of parallelization schemes which can scale linearly with the dimensions of problem at hand, i.e., the method is nearly "embarrassingly parallelizable". This opens the way towards scalable and performant distributed machine learning capabilities. The goal of this paper is to focus specifically on this very advantage, suggest various strategies for parallelization, and discuss their consequences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".