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Record W4400459006 · doi:10.21105/joss.06720

Catalyst: a Python JIT compiler for auto-differentiablehybrid quantum programs

2024· article· en· W4400459006 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Open Source Software · 2024
Typearticle
Languageen
FieldComputer Science
TopicQuantum Computing Algorithms and Architecture
Canadian institutionsXanadu Quantum Technologies (Canada)
Fundersnot available
KeywordsPython (programming language)CompilerProgramming languageComputer scienceDifferentiable functionMathematicsPure mathematics

Abstract

fetched live from OpenAlex

Catalyst is a software package for capturing Python-based hybrid quantum programs (that is, programs that contain both quantum and classical instructions), and just-in-time (JIT) compiling them down to an MLIR and LLVM representation and generating binary code.As a result, Catalyst enables the ability to rapidly prototype quantum algorithms in Python alongside efficient compilation, optimization, and execution of the program on classical and quantum accelerators.In addition, Catalyst allows for advanced quantum programming features essential for fault-tolerant hardware support and advanced algorithm design, such as mid-circuit measurement with arbitrary post-processing, support for classical control flow in and around quantum algorithms, built-in measurement statistics, and hardware-compatible automatic differentiation (AD).

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.284
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it