There are a few algorithms that are ubiquitous in the financial industry, and therefore ripe targets for seeking improvement using quantum computing. Monte Carlo programs, for example, are useful to estimate the likelihood of certain outcomes in processes where all the details are too complex to be accounted for exactly. In the context of financial analysis, Monte Carlo simulations can be used to predict the fair value of derivative instruments in various market scenarios. The cost of accuracy is, of course, computational power and time.
Monte Carlo simulations can be sped up using quantum computing. Classically, doubling the number of simulations doubles the amount of time required to get an improved answer. With a quantum computer, we can do better and reach the same, improved accuracy by generating fewer samples, thus substantially reducing computational costs.
Another case is of optimization. In many problems we are trying to minimize or maximize an objective function under some constraints, for instance, minimizing risk or maximizing profit. This is achieved by varying the parameters of the system we are constructing to reach the optimum. With a large number of parameters, often depending on each other in complex or unknown ways, it is challenging to devise computational techniques that find the optimum efficiency. As a consequence, we are left with the only option of trying all the possible solutions, which is often very computationally costly.
In these scenarios, quantum algorithms have been proven to beat classical algorithms making this sort of “parameter searches” much more efficient. Financial organizations are already beginning to investigate how they may take advantage of quantum computing, and the ones who will most benefit are those which have moved earliest. Potential applications include risk analysis, portfolio optimization, credit scoring, and derivative pricing, among others.
Cambridge Quantum Computing is collaborating with several financial institutions to develop a platform of financial quantum algorithms including those for Monte Carlo simulations, portfolio optimization, and encryption. Focusing on Proof-of-Concept cases, CQC is developing the core of these financial applications which can execute on today’s quantum hardware. As quantum hardware advances, it will be closely integrated with current IT systems: the applications are designed to be ready for hybrid quantum-classical production environments and to offer financial partners an immediate commercial edge.
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