In classic machine learning (ML) proven algorithms to be powerful tools for many tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computers and may have difficulty handling large files and complex data sets or to achieve a high degree of accuracy and precision.
Enter quantum machine learning (QML).
QML combines the power of Quantum Computation with the predictive power of ML to overcome the limitations of classical algorithms and offer performance improvements. In their article “On the role of entanglement in speeding up quantum computing,” Richard Jozsa and Neil Linden, of the University of Bristol in the UK, write that “QML algorithms promise to provide exponential speedups over their classical algorithms for some of the most tasks such as data classification, feature selection, and cluster analysis. In particular, the use of quantum algorithms for supervised and unsupervised learning has the potential to revolutionize machine learning and artificial intelligence.”
Continue reading… “Quantum machine learning (QML) is poised to make the leap in 2023”
