pasqal-io/qadence
Apr 2025 – Apr 2025
1 Month active
Languages Used
MarkdownPythonTOML
Technical Skills
Circuit DesignDocumentationPyTorchPythonQuantum ComputingQuantum Machine Learning
Arthut M. Faria
PROFILE
Arthut M. Faria
Arthur Faria developed core Quantum Convolutional Neural Network (QCNN) support for the pasqal-io/qadence repository, establishing foundational infrastructure for quantum machine learning and relational reasoning workflows. He implemented a dedicated QCNN class in Python, designed supporting circuit definitions and feature map creation, and introduced deferred observable handling to enable end-to-end QCNN pipelines. Arthur also authored comprehensive Markdown documentation to guide users through model construction and training. Leveraging skills in circuit design, PyTorch, and quantum computing, his work enabled customers to experiment with QCNN-based models in their quantum pipelines, expanding Qadence’s capabilities for advanced quantum analytics and user onboarding.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
545
Activity Months1
Work History
April 2025
2 Commits • 1 Features
Apr 1, 2025April 2025: Delivered core Quantum Convolutional Neural Network (QCNN) support within the Qadence framework, establishing a foundation for quantum machine learning tasks and relational reasoning workflows. Implemented a QCNN class, helper utilities, circuit definitions, feature map creation, and deferred observable handling, complemented by user-focused documentation that guides implementation and training. Business value: Enables customers to deploy QCNN-based models in their quantum pipelines, accelerating experimentation with quantum ML techniques and expanding Qadence's capabilities for advanced quantum analytics.
2 Commits • 1 Features
Apr 1, 2025April 2025: Delivered core Quantum Convolutional Neural Network (QCNN) support within the Qadence framework, establishing a foundation for quantum machine learning tasks and relational reasoning workflows. Implemented a QCNN class, helper utilities, circuit definitions, feature map creation, and deferred observable handling, complemented by user-focused documentation that guides implementation and training. Business value: Enables customers to deploy QCNN-based models in their quantum pipelines, accelerating experimentation with quantum ML techniques and expanding Qadence's capabilities for advanced quantum analytics.
April 2025
Activity
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Quality Metrics
Correctness95.0%
Maintainability90.0%
Architecture95.0%
Performance90.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPythonTOML
Technical Skills
Circuit DesignDocumentationPyTorchPythonQuantum ComputingQuantum Machine Learning
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