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.

First movers are already gaining an advantage in building expertise on how to model financial problems for quantum solutions.

QUANTUM COMPUTING DEMYSTIFIED

What is quantum computing?

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

Derivative pricing

Quantum computing can quadratically speed up risk calculations by improving sampling efficiency. For a 1 million-asset portfolio, quantum algorithms could reduce VaR calculation time from several hours to ~30 minutes. These methods could revolutionise risk management by enabling faster, more accurate modelling.

Portfolio optimisation

Balancing returns, risk and constraints in asset allocation is complex, especially with intricate dependencies. Quantum methods offer potential speeds up for certain optimisation problems and can better navigate non-convex landscapes, enabling more sophisticated portfolio strategies.

Anomaly detection

Anomaly detection involves analysing vast datasets for subtle patterns. Quantum machine learning could improve detection accuracy by recognising complex correlations that classical methods struggle to identify. These approaches could lead to faster, more accurate identification of anomalies in real-time applications.

Risk scoring

Accurately assessing risk requires calculating key metrics and identifying potential losses across various scenarios. New techniques could lead to more efficient and precise risk management strategies.

Asset price prediction

Quantum algorithms excel at tackling the Schrödinger-inspired partial differential equations used in derivatives pricing. By leveraging quantum phase estimation, these systems will price complex financial instruments like mortgage-backed securities and exotic options up to 1000x faster than classical methods.

Market simulation & forecasting

Quantum algorithms process parallel market scenarios to predict financial product adoption rates and investor behaviour patterns. These systems analyse vast sets of market sentiment data, trading volumes, and macroeconomic indicators simultaneously.

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.