By Kannan Agarwal

On 30 April this year, Goldman Sachs announced a “quantum computing breakthrough” in the pricing of complex derivatives. The algorithm in question is the Monte Carlo method, a complex mathematical technique widely used in finance to determine risk and simulate prices of financial assets.

Two months later, physicists at the University of Science and Technology of China (USTC) released three preprint papers on critical advances in quantum communication and quantum computing via arXiv.org, a free open-access archive for scholarly articles in science. The most significant of these papers, Strong Quantum Computational Advantage Using a Superconducting Quantum Processor, introduces Zuchongzhi, the world’s foremost quantum processor to corroborate and surpass the capabilities of a previous Google quantum computer capable of performing within 200 seconds a calculation which would take 10,000 years on a classical computer.

These back-to-back advances in the realm of quantum computing mirror the rivalry between the US and China in the geopolitical arena, with the latter currently in the lead. But what does all this really mean for banking and finance?

Our current computers (called ‘classical computers’ in the quantum computing world) typically take hours or even days to run complex calculations like Monte Carlo. For instance, in order to price derivatives, brokers currently let the algorithm run overnight and retrieve the results in the morning. This means that traders are making a call on risky financial instruments using outdated information.

While this isn’t much of a problem in a bearish market, it is a major risk when markets are bullish or volatile, like the current crisis. Deploying quantum algorithms mean that Monte Carlo simulations could be done 1000x faster and run repeatedly throughout the day, giving traders better information and faster results. It is obvious why achieving ‘quantum supremacy’ – when a quantum device solves an impossible problem for a classical computer – is the frontier in which many in finance are racing to conquer.

Even a millisecond advantage can translate into millions of dollars. It’s also been the subject of Hollywood movies like The Hummingbird Project, where two brothers race to lay fiber optic cables in order to shave milliseconds off of stock price deliveries from the New York Stock Exchange, piping it directly into their system in Kansas.

Needless to say, the winning team who cracks this holy grail can expect to see a surge in the performance of its mathematical models, reduced costs, and greater pricing precision.

What Is It?

Simply put, the ability of quantum computing to perform calculations at record speed is based on the strange properties of quantum mechanics discovered by Nobel Laureate physicists Niels Bohr and Max Planck – two of the founding fathers of quantum theory – as far back as 1910. Current classical computers store information in what we call ‘bits’ – 0’s or 1’s – which are stable and discrete. However, quantum computers use qubits, where data can exist in two different states simultaneously (see Spooky Action at a Distance on page 28). This exponentially accelerates computational power; quantum computers are millions of times faster than bit-based classical computers.

The quirky field has been called ‘finance’s next frontier’ because it can potentially deliver what the much-hyped AI and machine learning (ML) space promised but have fallen short.

For instance, in the realm of ethical AI, Pew Research reports that in March 2021, the University of Vienna released results from its experimental hybrid AI – a blend of quantum and classical computing – which was more than 60% faster than a nonquantum-enabled setup. The quantum-hybrid also led to greater diversity in how it handled a problem, a core stumbling block in AI-based decision-making which are subject to a large amount of embedded bias.

Qubit Gold Rush

Industrial behemoth Honeywell projects that quantum technology will be a USD1 trillion industry in the coming decades, with applications in all fields from aerospace to cryptography.

In a recent podcast aired by online media company Tearsheet, IBM’s Dr Stefan Woerner, Quantum Applications Research & Software Lead, and Goldman Sachs’ Will Zeng, Head of Quantum Research, estimate that as much as USD20 billion of public and private funds have come onstream to the quantum computing space, expanding the ecosystem and spurring a vibrant start-up community.

Although it is a paltry sum compared to the global dollars already raised by unicorns in the AI and ML space, it’s important to note that quantum computing is very much in its infancy. It is mostly at the proof-of-concept stage and researchers say that it will be another decade before we see a true general purpose quantum computer. That’s not stopped investors from rooting on quantum as a game-changer in the long run.

Zeng explains some of the big problems the team is looking to solve:

“The first is in, what I call broadly, simulations. Here, we work on the pricing of derivatives, what underlies it, our risk calculations. The math setup is calculating expectation values of stochastic processes or functions on stochastic processes, which come up all the time in finance. [It is] around this modelling that we’re pricing.

“The second is optimisation and there are a lot of hard optimisation problems across financial services, portfolio optimisation being the most obvious one, but they come up in all sorts of places.

“The third category is machine learning. Here, there are applications in trading but there are also applications in things like anti-money laundering or avoiding fraud.

“These three categories are already really, really large. What our research group is doing is trying to pick out more specific benchmarks where we think quantum computing could be most useful first.”

They aren’t the only ones. Entities like JPMorgan Chase and Wells Fargo have joined the fray with research partners like Honeywell and IBM. However, the incentive for China is far greater. The People’s Republic is already laying fibre-optic cables between Beijing and Shanghai, the foundation of its future quantum network. This has fuelled researchers at facilities like USTC to break the barrier in a field which could yield infinite applications, from hack-proof communications to precision pricing.

Table 1

Risk & Reward

In March 2021, the International Monetary Fund (IMF) published Quantum Computing and the Financial System: Spooky Action at a Distance?, its most comprehensive working paper to date on the subject.

Here’s a summary of how quantum can change the financial landscape (see Table 1).

Nonetheless, scientists in quantum computing are cautious of expressions like ‘revolutionary’ to describe its use, carefully avoiding the hype that’s characterised technologies like AI, where a surge of interest and investor dollars were followed by a cooler approach when it was discovered that the tech was not all it was cut out to be. In October 2019, Nature magazine voiced the doubt in many minds, quoting Dr Christian Weedbrook, founder of Xanadu, one of the leading quantum-computing companies in Canada: “There is still a lot of value being created — it’s just a case of whether there is too much hype.”

What’s certain is that time and again, history proves that we should approach every ‘next best thing’ the same way – with caution and a pinch of salt.

‘Spooky Action at a Distance’

Quantum computing is the use of quantum phenomena (such as superposition and entanglement) to perform computations. The basic unit of a quantum computer is qubit (short for quantum bit), typically realised by quantum properties of subatomic particles, like the spin of electrons or the polarisation of a photon. While each bit – its counterpart in classical computers – represents a value of either 0 or 1, qubits represent both 0 and 1 (or some combination of both) at the same time, a phenomenon called superposition.

Quantum entanglement is a special connection between pairs or groups of quantum elements, where changing the state of one element affects other entangled elements instantly, regardless of the distance between them. This is a counterintuitive phenomenon and Albert Einstein famously derided entanglement as “spooky action at a distance”. By entangling qubits, the number of represented states rises exponentially, making it possible to explore a huge number of possibilities instantly and conduct parallel calculations on a scale that is beyond the reach of traditional computers. Thanks to superposition and entanglement, adding just a few extra fully functioning qubits can lead to exponential leaps in processing power.

To reap the benefits of quantum computing, researchers need to build quantum machines that compute with lower error rates. Superposition and entanglement are fragile states. The interaction of qubits with the environment produces computation errors. Any external disturbances or noise, such as heat, light, or vibrations, inevitably yanks qubits out of their quantum state and turns them into regular bits. Classical computers are also prone to random computational errors, albeit at much lower rates. By employing redundancy, error correction processes enable classical computers to produce practical, error-free computations. However, such techniques are not applicable to quantum physics because of the no-cloning principles, it is physically impossible to copy the running state of a qubit.

For the foreseeable future, quantum computers are expected to complement, not replace classical computers. In the near future, quantum applications would probably be hybrid, since quantum and classical computing technologies have complementary strengths.

Source: Adapted from IMF, 2021

Kannan Agarwal is a Singapore-based researcher with Akasaa, a boutique content development firm with presence in Malaysia, Singapore, and the UK.