Background on Air Quality:
Air quality is one of the most critical indicators of environmental and public health. Poor air quality, driven by pollutants such as particulate matter (PM2.5), nitrogen dioxide (NO₂), and ozone (O₃), is directly linked to respiratory diseases, cardiovascular conditions, and premature mortality. According to the World Health Organization, air pollution is responsible for approximately 7 million deaths per year globally, making it one of the leading environmental health risks of our time. Monitoring and predicting air quality levels is therefore not just an academic exercise. It is a pressing real-world challenge with direct humanitarian consequences.
Traditional air quality monitoring relies on fixed sensor networks that provide localized, often sparse measurements. Machine learning offers a powerful complement to these systems by enabling predictive modeling at larger spatial and temporal scales, identifying hidden patterns in complex multivariate datasets, and classifying air quality conditions in near real-time. In this module, we explore how both classical and quantum machine learning approaches can be applied to an air quality and mobility dataset to classify whether conditions are acceptable or unhealthy.
Random Forest Classifiers:
Random Forest is an ensemble machine learning technique used for both classification and regression tasks. It works by constructing a large number of decision trees during training, each built on a random subset of the training data and a random subset of features, a process known as bootstrap aggregation, or bagging. For classification tasks, the final prediction is determined by majority voting across all trees; for regression, it is the average of all tree outputs.
The key strength of Random Forest lies in its ability to reduce overfitting, a common limitation of individual decision trees that tend to memorize training data rather than generalize from it. By introducing randomness at both the data and feature levels, Random Forest produces a diverse set of trees whose collective prediction is significantly more robust and accurate than any single tree alone.
Random Forest is widely used across many fields. In finance, it is applied to fraud detection and credit risk assessment. In healthcare, it assists in disease diagnosis and patient outcome prediction. In manufacturing, it enables predictive maintenance by detecting early signs of equipment failure. In environmental science, it is used to model climate patterns, predict energy consumption, and, as in this module, classify air quality conditions based on sensor and mobility data.
Quantum Machine Learning:
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines quantum computing with classical machine learning methods. Quantum computers exploit the principles of superposition, entanglement, and interference to process information in fundamentally different ways than classical computers, potentially offering computational advantages for certain classes of problems.
In the context of machine learning, quantum circuits can be used as trainable models, analogous to neural networks, where quantum gates with adjustable parameters are optimized to minimize a loss function. This is the basis of the Variational Quantum Classifier (VQC) used in this module. The VQC encodes classical data into a quantum state using an embedding layer, applies a series of parameterized entangling gates, and extracts a prediction from the expectation value of a measurement operator.
A key concept in QML is the quantum feature map, a quantum circuit that transforms classical input data into a high-dimensional quantum Hilbert space. This transformation can, in principle, capture correlations and patterns that are difficult or expensive to represent classically, particularly for datasets with complex nonlinear structure.
References:
Ambient air pollution, World Health Organization
https://www.who.int/news-room/fact-sheets/detail/ambient-(outdoor)-air-quality-and-health