In recent decades, classical machine learning (CML) has seen rapid development, allowing computers to generate reliable results accurately and quickly. In the last decade, as more powerful computers (both in software and hardware) and significant amounts of data become available, several breakthroughs in CML happened, including but not limited to AlexNet for image classifications, Gated Recurrent Unit for sequential predictions, BERT/transformer for natural language processing. However, there are natural limitations for CML that quantum machine. Thus, computer scientists turned their attention to quantum machine learning (QML), a new field that utilizes quantum properties to produce less time-consuming results while maintaining accuracy.

Tianle Yan. Quantum Machine Learning: Past, Present, and Future. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 12: 125-131. https://doi.org/10.25236/AJCIS.2023.061214.

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