Bibliographic record
Abstract
Overview This release integrates a cuQuantum backend, optimises distributed communication, and improves the unit tests. New features QuEST gained a new backend which integrates cuQuantum and Thrust for optimised simulation on modern NVIDIA GPUs. This is compiled with cmake argument -DUSE_CUQUANTUM=1, as detailed in the compile doc. Unlike QuEST's other backends, this does require prior installation of cuQuantum, outlined here. This deployment mode should run much faster than QuEST's custom GPU backend, and will soon enable multi-GPU simulation. The entirety of QuEST's API is supported! :tada: Other changes QuEST's distributed communication has been optimised when exchanging states via many maximum-size messages, thanks to the work of Jakub Adamski as per this manuscript. Functions like multiQubitUnitary() and mixMultiQubitKrausMap() have relaxed the precision of their unitarity and CPTP checks, so they will complain less about user matrices. Now, for example, a unitarity matrix U is deemed valid only if every element of U*dagger(U) has a Euclidean distance of at most REAL_EPS from its expected identity-matrix element. Unit tests now check that their initial register states are as expected before testing an operator. This ensures that some tests do not accidentally pass when they should be failing (like when run with an incorrectly specified GPU compute capability) due to an unexpected all-zero initial state. Unit tests now use an improved and numerically stable function for generating random unitaries and Kraus maps, so should trigger fewer precision errors and false test failures.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.133 | 0.169 |
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".