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
Developed core Quantum Convolutional Neural Network (QCNN) support for the pasqal-io/qadence repository, establishing foundational capabilities for quantum machine learning and relational reasoning workflows. Designed and implemented a dedicated QCNN class, supporting helper utilities, circuit definitions, and feature map creation using Python and PyTorch, with a focus on modular circuit design. Integrated deferred observable handling to enable end-to-end QCNN workflows within the framework. Authored comprehensive documentation in Markdown to guide users through QCNN construction and training processes. This work enabled customers to experiment with QCNN-based models in their quantum pipelines, expanding Qadence’s advanced quantum analytics capabilities for research and development.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
2Total
Bugs
0
Commits
2
Features
1
Lines of code
545
Activity Months1
Your Network
8 peopleShared Repositories
8
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
Repositories Contributed To
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