Quantum Computing and Its Use Cases in Investment Banking

By: Brandon Calalang

The rapid development of quantum computing is set to have profound effects on the financial services industry in the next 5-10 years. The integration of this remarkable technology will alter the way that investment banks and other financial institutions process data, make decisions, and manage risk. Utilising the principles of quantum mechanics, quantum computers can perform calculations and solve complex problems at speeds far beyond the capabilities of even our strongest classical computers. As this technology continues its development and investment banks gain access to quantum hardware and software, revolutionary solutions will emerge in areas such as market simulations, risk management, and cybersecurity.

An overview of Quantum Computing – How does it differ from classical computing?

A classical computer stores information in bits, which can represent a 0 or 1. By combining these bits into a binary code, a computer can take input from its user, process it according to its stored data, then produce an output.

When tasked with processing a large, complex problem, bits can become stuck with holding and processing large amounts of information, and this is where a quantum advantage can be realized. A key difference between classical and quantum computers is that quantum computers use qubits, as opposed to bits, which can store exponentially more data than the former.

Qubits are unique systems which are made of photons, electrons, trapped ions, atoms or a superconducting circuit which, due to the properties of quantum mechanics, can simultaneously store information as a 0, 1, or an amplitude of 0 and 1 (known as superposition). While classical computers process data sequentially and must compute every step of a problem to produce the desired output, quantum computers can process huge datasets simultaneously, which results in increased efficiency as the datasets being processed increase in size.

Quantum computers quickly find the most likely solution out of a range of possible solutions to a given problem, whereas traditional computers must ‘crunch the numbers' as they come, to produce a single answer in response to the given inputs. This advantage is best explained with the following analogy from IBM’s “What is quantum computing?” topic page:

“Imagine you are standing in the center of a complicated maze. To escape the maze, a traditional computer would have to ‘brute force’ the problem, trying every possible combination of paths to find the exit. This kind of computer would use bits to explore new paths and remember which ones are dead ends.

Comparatively, a quantum computer might derive a bird’s-eye view of the maze, testing multiple paths simultaneously and using quantum interference to reveal the correct solution. However, qubits don't test multiple paths at once; instead, quantum computers measure the probability amplitudes of qubits to determine an outcome. These amplitudes function like waves, overlapping and interfering with each other. When asynchronous waves overlap, it effectively eliminates possible solutions to complex problems, and the realized coherent wave or waves present the solution.”

Use Case: Market Simulations and Risk Management

A major use case of quantum computing in the broad finance industry, particularly in investment banking, is seen in market simulations. The Monte Carlo simulation is a commonly used financial modelling tool. This simulation is used by many financial analysts to predict the future prices of stocks, derivatives, among other asset types. Monte Carlo algorithms use randomly generated numbers to simulate numerous market scenarios under varying conditions.

The accuracy of these simulations is dependent on running as many scenarios as possible and accounting for as many meaningful variables as possible. Classical computers run these scenarios sequentially whereas quantum computers can run these simulations simultaneously, producing a more accurate output in a much shorter time frame. Additionally, quantum computers are well-suited for these market simulations as by nature, they are concerned with finding the most likely outcome. (explain this – connect this dot)

Improving the accuracy of financial market predictions from Monte Carlo simulations will help investment banks to better minimize risk and maximize profitability for their clients. Market conditions change rapidly, so even a slight advantage in forecasting market conditions could provide a competitive advantage for investment banks looking to retain and attract clientele.

Use Case: Fraud Detection and Cybersecurity

Today, most investment banks’ digital assets and financial data, including that of their clients, are protected through encryption algorithms. These algorithms use randomly generated numbers to create security keys. However, these keys are based on a mathematical algorithm which quantum computers can solve. Therefore, quantum decryption will pose a major risk to the security of financial data as this technology becomes more widely available and usable.

For investment banks dealing with multi-billion-dollar deals, the security and encryption of all data in a transaction is of the utmost importance. Seeing as quantum computers will soon be able to ‘crack’ these encryptions, banks must act to prevent their potential exposure to data breaches, fraud, and financial losses. Ironically, while quantum computing poses the threat, it simultaneously provides solutions to said threat.

One such solution is quantum key distribution (QKD) which uses quantum properties to encrypt communications between two parties exchanging encryption keys and detects any form of interference or interception attempt. If any such attempt is detected, the quantum state of the particles used will be altered and the investment bank will know that there has been a potential security breach. Currently, the encryption algorithms used to generate “random” security keys is based on an algorithm which quantum computers can solve and predict. On the flip side, quantum computers can generate truly random numbers utilizing the random nature of quantum particles. Therefore, quantum computers can be used to create uncrackable security keys to protect financial and other important data.

Therefore, as quantum technology continues to develop further, investment banks must continuously consider developing solutions such as QKD and quantum random number generation to encrypt communications and transactions.

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