Manoel Marques
New York, United States
Summary
🤩
Rockstar🎓
Top SchoolManoel Marques is a seasoned software engineer based in New York with nine years of experience building and leading application development in Web 2.0, mobile computing, and enterprise content management. He is fluent in Python, Java, Swift/Objective-C, C++, and JavaScript and comfortable across iOS and Android stacks as well as major IDEs like Xcode and PyCharm. Manoel has made substantive back-end and test-automation contributions to the prominent open-source Qiskit ecosystem, improving optimizers, refactoring tooling, and hardening unit tests for quantum computing libraries. His work often focuses on code quality, dependency management, and practical ML model features—skills reflected in fixes to linting, recursive dependency lookups, and QSVM enhancements. Drawing on an electronics engineering background and a master's in computer science, he blends low-level rigor with application-level product thinking. Notably, he frequently operates behind the scenes to keep complex scientific codebases maintainable and up-to-date with evolving APIs.
9 years of coding experience
Pontifical Catholic University of Rio de JaneiroBachelor, Electronic Engineering, Bachelor, Electronic Engineering at Universidade Federal do Rio de Janeiro
French, Portuguese, English
Stackoverflow
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Github Skills (28)
algorithm10
dependency-management10
unit-testing10
quantum-chemistry10
learn-ruby-on-rails10
python10
optimizers10
scikit10
testing10
user-manual10
codelist10
machine-learning10
basics10
lint10
optimisation10
Programming languages (9)
Github contributions (5)
qiskit-community/qiskit-machine-learning
Jan 2019 - Jan 2023
Quantum Machine Learning
Role in this project:
ML Engineer
ML EngineerContributions:6 releases, 87 reviews, 258 commits in 4 years
Contributions summary:Manoel contributed to the Qiskit Machine Learning repository by implementing and testing features related to Quantum Support Vector Machines (QSVMs). Their work involved modifying existing multiclass extension components and adding unit tests to ensure the integrity of the configuration. Further contributions include adding and testing methods for save/load model, minibatching with gradient support, and improving performance of machine learning models. This indicates a focus on the practical application of machine learning algorithms and model development within the quantum computing domain.
quantum-computingmachine-learningprototypequantum-programming-languagequantum
qiskit-community/qiskit-community-tutorials
Jan 2019 - Oct 2020
A collection of Jupyter notebooks developed by the community showing how to use Qiskit
Role in this project:Backend Developer
Backend DeveloperContributions:42 commits, 7 PRs, 1 comment in 1 year 9 months
Contributions summary:Manoel contributed to the Qiskit community tutorials by modifying several Jupyter notebooks to ensure compatibility with the Terra framework. These changes include updating code related to noise simulation, measurement error mitigation, quantum chemistry, quantum machine learning, and variational quantum eigensolver algorithms. The updates involved adjusting parameters, importing necessary modules, and adapting to changes in the underlying Qiskit API.
jupyter-notebooknotebooksjupyterqiskitjupyter-notebooks
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