Bibliographic record
Abstract
The release of QuTiP 4.3.1 Improvements MAJOR FEATURE: Added the Permutational Invariant Quantum Solver (PIQS) module (by Nathan Shammah and Shahnawaz Ahmed) which allows the simluation of large TLSs ensembles including collective and local Lindblad dissipation. Applications range from superradiance to spin squeezing. MAJOR FEATURE: Added a photon scattering module (by Ben Bartlett) which can be used to study scattering in arbitrary driven systems coupled to some configuration of output waveguides. Cubic_Spline functions as time-dependent arguments for the collapse operators in mesolve are now allowed. Added a faster version of bloch_redfield_tensor, using components from the time-dependent version. About 3x+ faster for secular tensors, and 10x+ faster for non-secular tensors. Computing Q.overlap() [inner product] is now ~30x faster. Added projector method to Qobj class. Added fast projector method, Q.proj(). Computing matrix elements, Q.matrix_element is now ~10x faster. Computing expectation values for ket vectors using expect is now ~10x faster. Q.tr() is now faster for small Hilbert space dimensions. Unitary operator evolution added to sesolve Use OPENMP for tidyup if installed. Bug Fixes Fixed bug that stopped simdiag working for python 3. Fixed semidefinite cvxpy Variable and Parameter. Fixed iterative lu solve atol keyword issue. Fixed unitary op evolution rhs matrix in ssesolve. Fixed interpolating function to return zero outside range. Fixed dnorm complex casting bug. Fixed control.io path checking issue. Fixed ENR fock dimension. Fixed hard coded options in propagator 'batch' mode Fixed bug in trace-norm for non-Hermitian operators. Fixed bug related to args not being passed to coherence_function_g2 Fixed MKL error checking dict key error
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.380 | 0.282 |
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".