Positive-Phase Temperature Scaling for Quantum-Assisted Boltzmann Machine Training
Bibliographic record
Abstract
Quantum-assisted sampling is a promising technique to enable training probabilistic ML models, which otherwise depend on slow-mixing classical sampling methods; such as, the use of Quantum Annealing Processors (QAP) to train Boltzmann Machines (BMs). Previous work has shown that QAPs can sample from a Boltzmann distribution, although, at an unknown instance-dependent temperature. Due to this distribution divergence, existing training algorithms have resorted to negative-phase temperature scaling. This method, although effective under arduous tuning, introduces unwanted noise to the sampleset due to the quantization errors caused by the underutilization of the QAP bias ranges; and is prone to bias overflow. We introduce a change in the training algorithm to allow positive-phase temperature scaling; an approach that reduces the impact of quantization noise, while still incorporating temperature scaling. As a result, we see an overall improvement in the convergence rate and testing accuracy, when compared to the state-of-the-art approach.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".