Quantum Machine Learning

Personal notes, references, and ideas at the intersection of quantum computing and machine learning.

About

About Me

A little about who I am and what I am working on.

Leonardo's profile picture

Hi! I'm Leonardo — a researcher and enthusiast exploring the frontier of quantum computing and machine learning. This site collects the resources I'm actively studying and the ideas I'm developing.

Feel free to reach out or follow my work.

Quantum Computing Machine Learning QML Research
Machine Learning

Machine Learning

Exploring machine learning through projects, research, and hands-on experimentation.

🎓
Master of Interdisciplinary Artificial Intelligence — University of Ottawa
uOttawa · In Progress
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🔗 Program

Master of Interdisciplinary Artificial Intelligence
uOttawa
→ View Program
📄
Important Papers
Landmark ML research · Reading list
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Attention Is All You Need
Paper
Vaswani et al. · Google Brain · NeurIPS 2017
Introduced the Transformer architecture, replacing RNNs with self-attention for sequence modelling. Foundation of all modern LLMs.
→ Read on arXiv
Deep Residual Learning for Image Recognition
Paper
He et al. · Microsoft Research · CVPR 2016
Proposed residual (skip) connections to train very deep CNNs. ResNet won ImageNet 2015 and remains a standard backbone.
→ Read on arXiv
Generative Adversarial Networks
Paper
Goodfellow et al. · Université de Montréal · NeurIPS 2014
Introduced the GAN framework — a generator and a discriminator trained adversarially to produce realistic synthetic data.
→ Read on arXiv
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper
Devlin et al. · Google AI Language · NAACL 2019
Showed that bidirectional pre-training of Transformers on large text corpora yields state-of-the-art NLP results via fine-tuning.
→ Read on arXiv
Playing Atari with Deep Reinforcement Learning
Paper
Mnih et al. · DeepMind · NeurIPS Workshop 2013
First deep RL model to learn control policies directly from raw pixels, combining CNNs with Q-learning (DQN).
→ Read on arXiv
ImageNet Classification with Deep Convolutional Neural Networks (AlexNet)
Paper
Krizhevsky, Sutskever, Hinton · University of Toronto · NeurIPS 2012
AlexNet kicked off the deep learning revolution by winning ImageNet 2012 by a large margin using GPUs and ReLU activations.
→ Read on NeurIPS
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Books I'm Reading
Self-study · In Progress
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Pattern Recognition and Machine Learning
Book
Christopher M. Bishop · Microsoft Research · 2006
→ Read PDF
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Book
Aurélien Géron · O'Reilly · 2nd Edition, 2019
→ View on O'Reilly
🎓
Data Science Program — Lighthouse Labs
Lighthouse Labs · Completed
▾

📝 Article

CPL: Machine Learning vs Real Result
Article
→ Read on Medium

💻 Projects

Statistical Modelling Project
GitHub
→ View on GitHub
Final Project — Tableau
GitHub
→ View on GitHub
Midterm Project
GitHub
→ View on GitHub
RnR — CPL Predictions
GitHub
→ View on GitHub
Supervised Learning Project
GitHub
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XGBoost Assignments
GitHub
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Unsupervised Learning Project
GitHub
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LHL Final Project
GitHub
→ View on GitHub
Quantum

Quantum Computing

Foundational references on quantum mechanics, quantum information, and quantum algorithms.

Quantum Computation and Quantum Information (10th Anniversary Edition)
Book
Michael A. Nielsen & Isaac L. Chuang · Cambridge University Press · 2010
The definitive reference for quantum computing — often called "Mike and Ike". Covers quantum circuits, quantum algorithms (Shor, Grover), quantum error correction, and quantum information theory from the ground up.
→ View on Amazon
QML

Quantum Machine Learning

Papers, tutorials, and resources bridging quantum computing with machine learning.

QML Roadmap 2026
QML Roadmap 2026
QML Roadmap 2026 — Trying to enter QML in 2026? This is the path.
LinkedIn
Kiran Kaur Raina · LinkedIn · 2024
A comprehensive visual roadmap for getting into Quantum Machine Learning in 2026 — covering prerequisites in quantum computing, classical ML, and the key QML topics to master.
→ View on LinkedIn