Doctor in Physics focused on applying classical and quantum artificial intelligence to solve optimization problems and differential equations. Researcher in a quantum communication company working on post-processing algorithms for continuous-variable quantum communication.

• 🖥️ Quantum Computing: Continuous variable quantum computing
• 🌌 Quantum Machine Learning: Quantum neural network
• 💡 Machine Learning: Pandas, seaborn, matplotlib, Scikit-learn, other
• 🧠 Neural Networks: TensorFlow, PyTorch
• 🎯 Problem Solving: Hard problems in physics, quantum mechanics, algebra, and advanced math. Numerical methods for solving differential equations

• 🐍 Python (+6 years)
• 🖋️ C++ (~1 year)
• ☕ Java (~6 months)

• Repository about Pytorch

  In this repository there are some introductory steps with step-by-step instructions on how to program a neural network for regression and classification problems, using CPU and GPU.

• Learning Machine Learning

  This repository is dedicated to my study of Machine Learning.

• Q-BASIC

  This repository contains my own programming language for quantum computation, quantum algorithm and quantum optics simulation make in Python.

• Feixes-pacialmente-coerentes

  This repository is a series of Jupyter notebooks dedicated to teaching how to simulate Partially Coherent Beam in Python.

• Quantum-Neural-Networks-in-Regression-Tasks

  Repository accompanying the article "Assessing the Advantages and Limitations of Quantum Neural Networks in Regression Tasks."

• Pinn-inverse-for-opem-quantum-system

  Repository for the article "Inverse Physics-informed Neural Networks Procedure for Detecting Noise in Open Quantum Systems."

• Notebooks_NN_Physics

  Repository for the article "Introduction to Neural Networks for Physicists."

• 📍SENAI CIMATE, Salvador, Bahia, Brasil.
• 🔗 Linkedin
• 🔗 GitHub Portfolio