Quantum Machine Learning

Quantum Machine Learning (QML) is an emerging, interdisciplinary field that integrates the principles of quantum mechanics with machine learning algorithms. It seeks to leverage the unique properties of quantum computation, such as superposition and entanglement, to develop novel algorithms that could potentially solve complex problems in artificial intelligence significantly faster or more efficiently than classical computers. Researchers in QML explore two main avenues: using quantum computers to accelerate existing machine learning tasks like optimization and data analysis, and applying classical machine learning techniques to better understand and control complex quantum systems.

1.
Foundations of Quantum Machine Learning
1.1.
Core Principles of Quantum Mechanics
1.2.
Fundamentals of Quantum Computation
1.2.4.
Key Quantum Algorithms
1.3.
Review of Classical Machine Learning
1.3.2.
Key Classical Models
1.3.2.3.
Tree-Based Methods
1.3.2.3.1.
Decision Trees
1.3.2.3.1.1.
Splitting Criteria
1.3.2.3.1.2.
Pruning Techniques
1.3.2.3.1.3.
Interpretability
1.3.2.3.2.
Random Forests
1.3.2.3.2.2.
Feature Randomness
1.3.2.3.2.3.
Out-of-Bag Error
1.3.2.3.3.
Gradient Boosting
1.3.2.3.3.1.
AdaBoost
1.3.2.3.3.2.
XGBoost
1.3.2.3.3.3.
LightGBM
1.3.3.
The Learning Process
1.3.3.1.
Data Preprocessing
1.3.3.1.3.
Feature Scaling
1.3.3.1.3.1.
Standardization
1.3.3.1.3.2.
Normalization
1.3.3.1.3.3.
Robust Scaling
1.3.3.1.4.
Data Splitting
1.3.3.1.4.1.
Training Set
1.3.3.1.4.2.
Validation Set
1.3.3.1.4.3.
Test Set
1.3.3.2.
Cost Functions and Loss
1.3.3.2.1.
Regression Losses
1.3.3.2.1.1.
Mean Squared Error
1.3.3.2.1.3.
Huber Loss
1.3.3.2.2.
Classification Losses
1.3.3.2.2.1.
Cross-Entropy Loss
1.3.3.2.2.2.
Hinge Loss
1.3.3.2.2.3.
Focal Loss
1.3.3.2.3.
Regularization Terms
1.3.3.2.3.1.
L1 Penalty
1.3.3.2.3.2.
L2 Penalty
1.3.3.2.3.3.
Elastic Net

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2. Core Concepts in Quantum Machine Learning