Quantum machine learning (QML) poised to make a leap in 2023
Classical machine learning (ML) algorithms have proven to be powerful tools for a wide range of tasks, including image and speech recognition, natural language processing (NLP) and predictive modeling. However, classical algorithms are limited by the constraints of classical computing and can struggle to process large and complex datasets or to achieve high levels of accuracy and precision.
Enter quantum machine learning (QML).
QML combines the power of quantum computing with the predictive capabilities of ML to overcome the limitations of classical algorithms and offer improvements in performance. In their paper “On the role of entanglement in quantum-computational speed-up,” Richard Jozsa and Neil Linden, of the University of Bristol in the UK, write that “QML algorithms hold the promise of providing exponential speed-ups over their classical counterparts for certain 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.”
QML versus classical machine learning
Zohra Ladha, senior director, data science and AI at Tredence, says QML differs from traditional machine learning in several key ways:
- Quantum parallelism: Quantum algorithms can take advantage of the unique property of quantum systems known as quantum parallelism, which allows them to perform multiple calculations simultaneously. When processing large quantities of data, such as images or speech, this can significantly reduce the time needed to solve a problem.
- Quantum superposition: Quantum superposition allows a quantum algorithm to represent multiple states simultaneously. This can enable it to explore possible solutions to a problem, leading to more accurate and efficient solutions.
- Quantum entanglement: Quantum algorithms can also use the property of quantum entanglement, which allows quantum systems to be correlated in ways that classical physics cannot explain. This can enable quantum algorithms to perform certain tasks more efficiently than classical algorithms.
Traditional machine learning algorithms, which rely on classical computing techniques and lack these quantum capabilities, may be slower or less accurate in certain cases.
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