PROBLEMS QUANTUM SOLVES
Financial Services
The financial services industry is anticipated to be the first to reap the benefits of quantum computing, due to its distinct ability to handle vast amounts of data securely, while solving complex problems and removing barriers.
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Quantum computers, instead of using bits, rely on qubits, which can store more information and process it simultaneously through a phenomenon called entanglement, giving them much greater potential for complex problem-solving. The fault-tolerant quantum computers of the future will have the potential to revolutionise most aspects of the financial services industry.
Some of the most commonly-cited quantum financial solutions include running Monte Carlo simulations with considerably more efficiency, thus enabling greater accuracy in financial forecasting and analysing irregular behaviours which will enabling improved fraud detection.
Use Cases
Security & encryption
Through it’s ability to transform data encryption, quantum computing can improve financial transaction security by shielding sensitive data from prospective attackers. Similarly, quantum will generate more accurate and efficient at detecting trends, anomalies, and potential fraudulent actions: ultimately lowering the risk of financial loss for the entire financial services industries and it’s customers.
Portfolio management
By improving business optimisation and finding the optimum portfolio of assests to maximise returns, quantum will help the financial services industry reshape how investment portfolios are constructed and managed. This will unlock unprecedented insights, efficiencies, and opportunities for investors and institutions, all while considering market volatility, asset correlations and transaction costs.
Insurance premiums
With it’s unique ability to process vast amounts of data and perform complex calculations, quantum computing could revolutionise the way risk is assessed and priced. By analysing a wide range of factors, including customer demographic, health records, historical claims and market trends, insurance companies will be able to develop more accurate and precise actuarial models.
Large-scale flood modelling demands a quantum solution.
Shallow Water Equations (SWE) are used to predict water flow in rivers, oceans, and other bodies of water. Modelling SWEs involves creating computer-based models incorporating various factors, ranging from simplified models to more complex ones that consider flooding through the use of multiple factors. The computational cost of running such simulations over large areas poses limitations.
As part of a technical feasibility study, Multiverse Computing and Moody’s Analytic explored the Quantum Physics-Informed Neural Network (QPINN) algorithm to address the computational challenges in large-scale flood modelling studies.
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