How quantum will enhance machine learning in finance
Our Head of Machine Learning William Clements wrote an article for Finance Derivative discussing how quantum can enhance machine learning in the financial sector.
Quantum machine learning (QML) combines the principles of quantum computing with advanced machine learning algorithms to help financial organisations improve generative modelling, analyse large volumes of data, and make more informed decisions.
While they promise to solve complex financial problems more efficiently and accurately than classical approaches, the applications are still at an exploratory stage. Potential uses include creating synthetic data for generative modelling and optimising investment strategies.
Generative modelling to create synthetic data
QML holds the potential to enhance the creation of synthetic data for generative modelling and generate realistic and diverse datasets that mimic the characteristics of real financial data. By exploiting quantum principles like superposition and entanglement, these algorithms could capture complex patterns, and produce high-dimensional synthetic data samples. This advancement could open new avenues for training novel generative models such as hybrid quantum/classical GANs, improving their fidelity, and enabling the development of more realistic and diverse synthetic datasets for various applications.
Improving portfolio optimisation
QML is emerging as a useful tool to improve machine learning, as it provides new methods of learning relationships within data. This helps traditional machine learning algorithms process large-scale complex datasets, enabling more comprehensive and accurate portfolio optimisation.
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