Sahil Gaikwad and Wonjun Lee

# The good, the bad, and the maybe’s of quantum finance

Updated: Jul 16

*Recent advancements in quantum computation *have* attracted attention from across the spectrum of the corporate world, especially top financial conglomerates. With the likes of JP Morgan and Goldman Sachs investing heavily into quantum computation, we explore the potential of this new technology in finance, investigating how it can revolutionalise industries such as algorithmic trading and risk management.*

Quantum computing is a rapidly advancing field that has the potential to revolutionise the way we process and analyse data. In the realm of finance, quantum computing promises to provide powerful tools for financial modelling and risk assessment. However, it is not a straight path to success. This article will explore the benefits and harms of quantum computing and debate whether the financial sector will ever come to the point of requiring such a futuristic system.

The widespread adoption of Quantum Computing can benefit finance in many ways. Possibly its greatest advantage is that it can simulate and optimise large datasets, as well as interpret them. This gives investors a major edge when making financial forecasts. Through algorithms such as the Quantum Annealing algorithm, traders can determine the combination of assets to invest in to achieve a desired return whilst managing __risk__. Similarly, strategies that are currently being used to minimise risk can be drastically improved by combining them with quantum algorithms. For instance, the Monte Carlo method of risk management, which models the future performance of an investment portfolio by considering several parameters, can run significantly faster and more efficiently if it is supercharged with quantum __algorithms__.

Quantum computers' performance will also provide benefits for machine learning. In the finance sector, this will allow such algorithms to analyse and make interpretations on larger and more complex datasets. In particular, improved machine learning may help identify fraudulent transactions by detecting suspicious patterns that correlate with such activity and preventing them before they __occur__.

**Hurdles for quantum computing**

Despite the exciting future of quantum computing, there remain physical and ethical hurdles preventing the commercial application of the technology. Quantum computers are currently in a very early stage of development: the largest quantum computer only holds 400 qubits (the equivalent of a ‘bit’ in classical computing). It is estimated that at least a million qubits will be necessary to achieve commercial __relevance__.

However, a million-qubit quantum computer, there would still face problems. Most concerningly, powerful quantum systems will be able to render encryption protocols like the RSA method useless. The RSA algorithm relies on the fact that a classical computer will be unable to prime factorise a large product of two prime numbers within a feasible amount of time. However, this is not a barrier for quantum computers. Similarly, digital mulling, which has long been used to launder money by criminals, can be enhanced to a dangerous degree with quantum computing. Today, authorities detect certain flags in money movements that criminals commonly use; however, quantum computers would be able to implement common legitimate money-moving patterns to obfuscate any suspicious __patterns__. Hence, even though encryption protocols like RSA are predicted to be uncrackable until the __2030s__, banks are having to invest heavily in developing new quantum cryptography methods.

There are also several ethical problems that could arise within the finance industry from quantum computing. Perhaps the biggest problem would be an increased level of discrimination. Organisations and businesses that have access to a quantum computer would have a decided advantage against those that do not. Since conventional strategies that smaller companies would employ would become ineffective, the likelihood that they could afford a quantum computer would __decrease__. Another issue that may arise is the monopolisation of certain quantum algorithms, especially combined with intellectual property claims. A company specialising in developing quantum trading algorithms could choose to sell only to specific banks, leading to competition and antitrust issues. These examples show the importance of updating financial regulations to reflect the paradigm shift of quantum computing as the technology is commercialised.

**Is there a case for quantum computing in the finance sector?**

But will quantum computing ever be necessary for the finance sector? As the financial industry continues to evolve and adapt to new technologies, the question of whether quantum computing will ever be necessary for Fintech or Banking has become a topic of increasing interest.

In the 21st century, financial institutions have resorted to using powerful supercomputers for processing vast amounts of data at incredibly high speeds. In particular, they are used in high-frequency trading (HTF) and Algorithmic trading. Algorithmic trading describes the use of computer programs to execute trades automatically on financial markets. These algorithms are based on mathematical models and statistical analysis that consider a variety of factors such as market conditions, historical data, and other trading strategies. High-frequency trading is a type of algorithmic trading which involves placing a high volume of orders within a market in an extremely short space of __time__.

__High frequency trading as a percentage of all US equity trading__

Today, algorithmic trading has become an integral part of financial markets due to its great efficiency in trade execution and lowering transaction costs. 65-70% of overall trading volumes in the US, European and major Asian markets are __algorithmic__. Yet despite an increasing concentration of these computationally-intensive financial products, it is unclear that we are yet to hit a wall with "classical" computation. In their current capacity, quantum computers can already solve computational calculations that take classical computers thousands of __years__ (famously, Google achieved quantum supremacy in 2019, solving a calculation in three minutes that a classical supercomputer would take 10,000 years to complete). However, whether maintaining an edge in algorithmic trading will demand his computational power in the near future remains to be seen.

Financial institutions, such as Goldman Sachs and JP Morgan Chase, are making active contributions to quantum research because they see the financial benefits it can add to existing systems in the near future, as well as the long-term benefits quantum computation can add by __itself__. They predict that in the next decade, early quantum computers will generate a modest income of $2 billion to $5 billion for financial institutions worldwide. As quantum computers become stable and error correction improves, the same institution could unlock a value of up to $900 million in the following 15 __years__. For now, though, we will have to wait to see the true effects of quantum finance in the banking industry until quantum computers are demanded in trading strategies and experience wider commercial application.