The stage is set for quantum computing to bring about seismic changes in the world as we know it. As IBM and Google, among others, publicly race to develop quantum computers that can solve a real-world problem with an acceptable rate of error, a burgeoning body of research points to their transformative potential. From radically scaling our ability to model complex processes such as the progress of diseases and climate change, to optimising AI and securing the integrity of sensitive data against attack and misuse, the possible uses of quantum computers are significant and wide-ranging.
What is quantum computing?
Put simply, quantum computers are computers which make use of quantum mechanics, a branch of physics which deals with the behaviour of very small objects such as electrons. When objects are very small, they behave in unintuitive ways, and it is these properties that are harnessed by quantum computers. In particular, quantum computers make use of two laws from quantum mechanics: superposition and entanglement. Superposition refers to the ability of individual units to exist in several possible states at the same time (you may recall Schrödinger’s cat, a thought experiment used to illustrate this phenomenon). A quantum computer makes use of superposition through quantum bits or “qubits”. A classical computer, like the one you are perhaps reading this article on, makes use of bits, units of information which can only exist in one of two states: off or on, 0 or 1. By contrast, a qubit can be on, off, or on and off in a variety of combined states at a single point in time. The state of a series of qubits can also become linked even if physically separated. In quantum mechanics this is referred to as “entanglement” – a law that Albert Einstein himself once famously dismissed as “spooky action at a distance”.
Quantum computers will make use of superposition and entanglement to process information, identify causal relationships and tackle particular (although not all) calculations with greater efficiency and at greater speeds than a classical computer – not least because they should be able to solve multiple problems or calculations in parallel. As a result, it is projected that quantum computers will speed up the rate at which certain machine learning tasks are performed and enable us to better analyse and model the highly complex data sets underpinning biological, chemical, human, financial and other systems. It is also anticipated that, when powerful enough to run an algorithm called “Shor’s algorithm”, quantum computers will be able to crack certain cryptographic codes in tiny fractions of the timescales needed by classical computers. Significantly, this could render the widely used RSA encryption standard vulnerable.All of this is to say that while the advent of quantum computing presents some significant risks and vulnerabilities, particularly with regard to cybersecurity, it also presents the possibility of fantastic leaps in our abilities. For example, to better predict (and forestall) climate change, to model the progress of diseases such as cancer and to respond more effectively to complex humanitarian events.
Implications for the banking sector
It is no exaggeration to state that quantum computing has the potential to transform risk management in the banking sector. One key potential area of impact will be around the modelling of financial markets. Increased insight into market dynamics and behaviour could be leveraged not only by participants but also by regulators and governments as they oversee the banking sector and the health of the economy. Through the use of better models, generated and analysed by quantum computers, areas of weakness or developing risk might be better and earlier identified and then targeted. Such modelling could be further drilled down to the level of customer dynamics and used by financial institutions and regulators to illuminate and predict customer behaviour, providing risk trend insights and the opportunity to target offerings with greater fairness and precision. What is true for the broader financial markets is also true for institutional balance sheets. Moreover, at a time when the PRA and FCA are increasingly focussed on operational resilience, the ability of banks to model their internal processes and workflows, and to far quicker identify areas of weakness under stress is invaluable. More generally, in a data rich sector quantum computing promises the potential for banks and investment firms to process data in a much more effective and energy-efficient manner. This has implications ranging from improving the customer experience of banking services, to detecting fraudulent transactions. Finally, much has already been written about the risk quantum computers pose to current encryption practices. This risk clearly merits serious thought: compromised cybersecurity has ramifications under both data protection legislation and (future) regulatory rules surrounding operational resilience. This risk can also, however, be overstated, as quantum technology equally offers an opportunity to devise super-secure methods of encrypting data.
Timelines and conclusions
Basic quantum computers already exist, and some have achieved a “quantum advantage”, whereby they can perform a particular task faster than a classical computer. They are not yet proven, however, to be ready to solve “real-world” practical problems faster than classical computers, and they are prone to high rates of error. Some commentators believe that quantum computers will be in mass use by governments and companies by the 2030s. This is a relatively near horizon, and so the topic bears consideration among lawyers and risk managers today. Quantum technologies have the potential to have a radical impact on the design and long-term effectiveness of today’s systems, processes, models, and security standards, and merit serious attention from those in the banking sector.
This article was originally published in the April 2020 issue of Butterworths Journal of International Banking and Financial Law
