William Clements, Head of Machine Learning, ORCA Computing
Quantum computing is a rapidly developing field at the intersection of physics and computing. Armed with the ability to solve certain types of problems exponentially faster than its classical counterparts, it has the potential for impact in a wide range of industries, including finance.
One area that is showing promise in finance is quantum machine learning (QML). This 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
By leveraging advanced machine learning techniques, generative models facilitate the creation of synthetic data, which can be used to augment limited datasets, enhance privacy protection, and develop robust trading strategies. Creating synthetic data is also essential to train and evaluate machine learning models in scenarios where real data may be scarce, sensitive, or difficult to obtain. To support innovation in the financial industry, the financial conduct authority (FCA) provides a sandbox environment with high-quality synthetic data for organisations to use. The process of creating synthetic data involves leveraging techniques such as generative adversarial networks (GANs), which consist of two neural networks: a generator and a discriminator. GANs are designed to generate new data samples that resemble a given training dataset. The generator network learns to produce synthetic data, while the discriminator network learns to distinguish between real data from the training set and the synthetic data generated by the generator.
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
Machine learning is a powerful technology that can be used to improve the performance of portfolios, from identifying factors that predict future returns to constructing portfolios that are diversified and risk-adjusted and rebalancing portfolios on a regular basis. By leveraging advanced algorithms and data analysis techniques to enhance risk assessment, asset selection, portfolio rebalancing, and market prediction, investors can make more informed decisions, improve portfolio performance, and adapt to the dynamic nature of financial markets.
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. It can also assist in solving the combinatorial explosion problem commonly encountered in portfolio optimisation. As the number of assets and possible portfolio combinations grows exponentially, classical computing methods face limitations in exploring all possible solutions. QML algorithms have the potential to come up with better solutions in some cases. This could potentially lead to more sophisticated portfolio construction strategies that consider a broader range of factors, resulting in improved risk-adjusted returns.
Working towards a quantum future
The convergence of quantum computing and machine learning holds the potential to accelerate innovation in the financial industry through its ability to create synthetic data for generative modelling, process significant amounts of data and perform some complex calculations quickly. Despite the foreseeable benefits, practical implementation is still in its early stages. The field requires further research, development, and refinement to overcome challenges such as scalability, but there is an expectation from the quantum industry to demonstrate narrow advantage over classical approaches in the relative near-term. For their part, financial services organisations are actively exploring and investing in QML to enhance their operations, improve decision-making, unlock new insights, and gain a competitive edge over the longer term.
As the field progresses, continued collaborations between experts in quantum computing, machine learning, and financial organisations will be crucial to drive innovation and overcome the current challenges. Even though the road ahead may be complex, the possibilities are endless, and the impact on finance holds the promise of changing how we understand, predict, and manage financial systems.
