Financial Services and Quantum Computing: Revolutionizing Risk Assessment and Market Prediction

Abstract

The continually expanding financial markets often require a more advanced approach to calculation to cope with high dimensionality of risk variables and unstable market environments (Orus et al., 2019). One of the potential computational paradigms that has appeared and could offer an effective solution to these challenges is quantum computing where quantum parallelism and quantum entanglement are used to more efficiently process large volumes of data space compared with classical systems (Preskill, 2018). It can be already seen in the development of financial services technological research, which investigates the feasibility of quantum-classical hybrid algorithms to transform risk assessment and market forecasting. Following recent advances of quantum machine learning (QML) and quantum optimization (Egger et al., 2020), we developed an experimental study of the ways how quantum variational algorithms could be combined with the classical predictive models based on historical market data (Hegade et al., 2021). The outcome of our research shows that by increasing simulation of the scenarios and more accurate assessment risk through the portfolio hybrid pipelines can be more effective than the conventional model in terms of utilizing less execution time. This paper is part of the body of literature surrounding the field of quantum finance and presents the most important steps to follow in the future study to achieve the deployment of noise resistant algorithms and increased benchmarks (Barkoutsos et al., 2020; Wang & Lee, 2021). Keywords: Quantum Computing, quantative actively predicting risk assessment and also quantative actively predicting market via quantative machin learning, with quantum variational algorithms privacy to quantify/M hybrid tactical.