Structural Modifications in Quantum-Assisted Training for General Boltzmann Machines
Bibliographic record
Abstract
Quantum-assisted training of machine learning models makes use of the sampling capabilities of quantum computing devices to approximate the probability distribution of classical generative machine learning models. One example of this is the use of quantum annealers to sample from a Boltzmann distribution to assist the computation of gradients required to train Boltzmann machines (BMs). Doing so requires the interactions and weights of a BM to be mapped onto the underlying qubit connectivity built into the quantum device hardware. This opens the possibility of formulating any graph topology as a BM, instead of the more common classical approach of restricting their connectivity; i.e., Restricted Boltz-mann Machine (RBM). This work is focused on simultaneous modifications of the connectivity and qubit mapping of general BMs, which we refer to as structure-aware minor-embedding. We show how minor-embedding can be performed taking into account the nature of the BM. We also evaluate weight “pruning”, which is motivated by the intuition that, when a QAML model is pruned, the resulting qubit chains resulting from the minor-embedding methods can provide better quality samples, at the expense of a smaller model.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".