Parametricdft.Jl

B 84 completed
Library
unknown / markdown · small
58
Files
31,244
LOC
0
Frameworks
4
Languages

Pipeline State

completed
Run ID
#366659
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Skipped
Decision
skip_scaffold_dup
Novelty
43.00
Framework unique
Isolation
Last stage change
2026-04-16 18:15:42
Deduplication group #47403
Member of a group with 1 similar repo(s) — canonical #349 view group →
Top concepts (2)
Project DescriptionDocumentation
All rows scored by the Repobility analyzer (https://repobility.com)

AI Prompt

Create a Julia library called ParametricDFT that learns parametric quantum Fourier transforms using manifold optimization. The project should include examples and documentation. I need a setup that allows users to easily get started, ideally with a Makefile to handle initialization, testing, and running examples. Please structure it to support a variational approach for approximating the Discrete Fourier Transform (DFT) using parameterized quantum circuits.
julia quantum-computing optimization dft library manifold scientific machine-learning
Generated by gemma4:latest

Catalog Information

A Julia library that learns parametric quantum Fourier transforms through manifold optimization.

Description

ParametricDFT is a Julia package that implements a variational approach to approximate the Discrete Fourier Transform (DFT) using parameterized quantum circuits. It leverages manifold optimization techniques to train the circuit parameters, enabling efficient learning of quantum Fourier transforms. The library provides a set of utilities for constructing quantum circuits, defining cost functions, and running optimization loops. It is designed for researchers and developers who want to explore quantum algorithm design and study the interplay between quantum circuits and continuous optimization. The package includes example scripts and a test suite to validate its functionality.

الوصف

تُقدِّم مكتبة ParametricDFT حلاً برمجياً يُمكّن الباحثين من تعلم تحويلات فورييه الكمّية المتدرجة عبر استخدام خوارزميات تحسين المنحنى. تُبنى المكتبة على نهج متغير يقترب من تحويل فورييه المتقطع (DFT) باستخدام دوائر كمّية مُعلمة، وتُطبِّق تقنيات تحسين المنحنى لتدريب معلمات الدوائر. تتضمن المكتبة أدوات لبناء الدوائر، وتحديد دوال التكلفة، وتشغيل حلقات التحسين، مع دعم للمعالجة العددية الدقيقة. تستهدف المكتبة علماء الفيزياء الحسابية ومطوري الخوارزميات الكمّية الذين يرغبون في استكشاف تصميم الخوارزميات الكمّية وفهم العلاقة بين الدوائر الكمّية والتحسين المستمر. كما توفر مكتبة ParametricDFT أمثلة عملية، ومجموعة اختبارات للتحقق من صحة الأداء، وتدعم بيئة تطوير متكاملة لتسهيل تجربة المستخدم.

Novelty

7/10

Tags

quantum-fourier-transform variational-quantum-circuits manifold-optimization parametric-modeling numerical-simulation discrete-fourier-transform quantum-algorithm-design optimization-techniques

Claude Models

claude-opus-4.6 claude-sonnet-4.5 claude-opus-4.5

Quality Score

B
84.4/100
Structure
80
Code Quality
100
Documentation
59
Testing
85
Practices
78
Security
100
Dependencies
50

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (1200% test-to-source ratio)
  • Consistent naming conventions (snake_case)
  • Low average code complexity \u2014 well-structured code
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Recommendations

  • Add a linter configuration to enforce code style consistency

Security & Health

4.1h
Tech Debt (A)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (0)
Open data scored by Repobility · https://repobility.com
MIT
License
0.0%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

markdown
80.8%
yaml
10.7%
toml
7.1%
shell
1.4%

Frameworks

None detected

Concepts (2)

Repobility · code-quality intelligence · https://repobility.com
CategoryNameDescriptionConfidence
Repobility — same analyzer, your code, free for public repos · /scan/
auto_descriptionProject Description![Stable](https://nzy1997.github.io/ParametricDFT.jl/stable/) ![Dev](https://nzy1997.github.io/ParametricDFT.jl/dev/) ![Build Status](https://github.com/nzy1997/ParametricDFT.jl/actions/workflows/CI.yml?query=branch%3Amain)80%
auto_categoryDocumentationdocs70%

Quality Timeline

1 quality score recorded.

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