A new study published by researchers at the University of British Columbia suggests that quantum machine learning algorithms can outperform classical machine learning algorithms. The researchers tested the quantum and classical algorithms on a range of tasks and found that the quantum algorithm consistently performed better than the classical algorithm in terms of accuracy and efficiency. This study shows that a quantum advantage exists for two of the most popular quantum machine learning classification models: Variational Quantum Classifiers (also known as quantum neural networks) and Quantum Kernel Support Vector Machines. But, as one of the authors notes, the key challenge now 'is to find a real-world machine learning application that would benefit from this quantum advantage in practice'.