Dr. Yini Airet Miro, a researcher at the Universidad Internacional Iberoamericana (International Iberoamerican University, UNIB), is participating in a study that explores the use of quantum machine learning (quantum SVM) in tracking the impact of air pollution on human health and the environmental factors that contribute to the problem.
Air quality is an issue of global concern due to its detrimental effects on human health and the environment. Air pollution, caused by a variety of factors such as pollen, dust and noxious gasses, can have a significant impact on quality of life. The air quality index (AQI), which classifies air quality into different categories, is used to assess air quality. Particulate matter (PM2.5, PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3), among others, are also used to measure air quality. An AQI of 50 or less is considered “moderate”, while an AQI above 300 is considered “hazardous”.
When the AQI is high, people are advised to stay indoors and close doors and windows to protect themselves from the harmful effects of pollution. According to WHO, almost everyone in the world breathes polluted air. Fine particles, such as PM2.5, pose a health risk because they can penetrate the lungs and contain harmful particles such as viruses. Prolonged exposure to these particles has been associated with an increased risk of respiratory and cardiovascular diseases, including COVID-19.
The use of machine learning methods has proven to be effective in predicting air quality, as they can handle large data sets and generate accurate forecasts. Algorithms such as neural networks (ANN), support vector machines (SVM), random forest (RF) and K-nearest neighbor (KNN) have been used to make predictions in this field. In recent years, a new paradigm has emerged by combining quantum mechanics and machine learning, giving rise to quantum machine learning to improve the efficiency of computational systems and tackle difficult data analysis problems.
Given the implications of air pollution on human health, it is important to determine the environmental factors that contribute to air pollution. Therefore, the aim of the study in which Dr. Miró participated was to improve the performance of quantum SVM in order to predict how environmental factors affect the levels of air pollution that affect health and compare the results with the application of traditional SVM.
The research results showed that quantum SVM performs much better than the traditional SVM model for air quality assessment, achieving 97% and 94% accuracy, while the traditional model achieved 91% and 87% accuracy. However, it is important to recognize that quantum computers are still in their early stages of development, therefore, in the near future, when cubits with less noise and error corrections are increased, quantum computers will achieve better results in terms of time and accuracy. For this, more research is needed in the coming years.
If you want to learn more about this study, click here.
To read more research, consult the UNIB repository.
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