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In 1975, Gordon Moore, the founder of Intel, predicted that the number of transistors on an integrated circuit would double every two years, creating more and more powerful microchips at ever smaller sizes. His prediction, known as Moore’s Law, came true. And utterly transformed our world.
Eventually though, physical limits caused that exponential growth to plateau. Meanwhile, the demand for increased processing power is greater than ever. So researchers are racing to find alternative ways to move forward. One possible solution might be to enter the quantum realm. Another might be to build supercomputers based on optical circuits. At Barclays’ Chief Technology Office, we’re right there at the forefront, researching both.
Quantum physics describes the unpredictable, almost magical, way that sub-atomic particles behave. Which is why quantum computers often feel like they defy day-to-day logic. Classical computers rely on binary circuits and, at any given time, every ‘bit’ in the computer represents one of two possible states – a 0 or a 1.
Quantum computers encode information in ‘qubits’. They are based on subatomic particles that obey the laws of quantum mechanics, where matter can exist in a superposition of multiple states at the same time. This is a probabilistic world where particles can exist in a number of places or states simultaneously until they are observed. So each qubit represents a superposition of both 0 and 1 at the same time.
As a result, a collection of qubits can contain exponentially more information than the same number of classical bits. In a quantum computer circuit, with many entangled qubits in superposition, it’s possible to perform calculations on far more variations of each item of data at the same time. This gives quantum computers the potential to perform complex calculations in a matter of seconds that could take years on a classical computer. And it’s fair to say that the entire tech world is waiting to see what happens when someone finally lets Schrödinger’s cat out of the bag!
Little surprise then, that the UK Government recently announced its new National Quantum Strategy, backed with £2.5 billion government funding over the coming decade. And that Barclays’ Chief Technology Office has been investigating quantum computing since 2017.
The person spearheading that research is Lee Braine, Managing Director and Distinguished Engineer in Barclays’ Chief Technology Office.
“We are particularly excited at the prospect of using this technology in the future for financial market analysis to make better predictions about how markets will respond to different events,” says Lee.
Lee’s team focuses on investigating the viability and suitability of advanced technologies for the bank. “We recognised quantum computing’s huge potential early on. But there’s a catch. Quantum computers are still very much at the research stage.”
Quantum computers are incredibly sensitive and complex machines that operate in hard-to-maintain conditions, such as near absolute zero temperatures. It’s very difficult to keep a large number of qubits in a coherent and entangled state, which means current machines have too few qubits to be of practical use. Right now, researchers are still focused on building experimental quantum computers and developing quantum algorithms tailored for them.
“It’s important to understand how quantum algorithms can be applied to business-relevant problems even though real-world challenges cannot be tackled with today’s hardware. We have conducted specific tests to learn how we could potentially leverage quantum computing in future at Barclays,” explains Lee.
Lee’s first experiment focused on how quantum computing could optimise the settlement efficiency of batches of securities transactions. Optimisation problems are mathematical challenges that require finding the best solution from a set of feasible alternatives. They underpin many of society’s most important industries, from finance to healthcare, logistics and manufacturing.
The optimisation problem Lee set is a netting process where different obligations between firms are balanced off against each other at financial clearing houses. The magnitude of the task is staggering, with hundreds of thousands of transactions daily. In 2022, a single clearing house, DTCC and its subsidiaries, processed transactions worth $2.5 quadrillion. (One quadrillion is equal to one thousand trillion.) Like most optimisation problems, it’s the sheer scale that foils the capacity of classical computers to solve it.
“It’s a problem that’s almost impossible to solve perfectly every time. And, effectively, it could take the lifetime of the universe to evaluate all the possible options,” says Lee. “Currently, a variety of computing and mathematical shortcuts are used to make sophisticated estimations. It’s an important optimisation challenge in capital markets, as it can cut the time between trade and settlement and reduce risks significantly.”
Lee’s conclusion on quantum computing was double-edged. “We showed that, with the right quantum algorithms, quantum computers can handle this optimisation challenge for small-scale data sets. But it's going to be quite a few years before quantum computers can handle real-world data sets.”
Experts are split on when, if ever, we will be able to build quantum computers large enough to solve real-world problems. Meanwhile, some researchers are looking to the past to create the future. Before the digital revolution really took shape, people investigated using optical circuits for computing. Now, with Microsoft Research’s Project AIM (Analog Iterative Machine) that approach is back.
If you replace classical electrical circuits with optical ones, you switch from processing data using electrons to using photons. And that allows for calculations at up to the speed of light. However, optical computers won’t be as flexible as digital computers. But for some tasks, like hard optimisation problems, they may be much quicker than digital supercomputers, and more practical than quantum computers.
Optical computing is also very much at the research stage. In fact, the Project AIM computer is currently built on a metal bench the size of a dining table at the Microsoft Research Lab in Cambridge. However, unlike quantum computing, their optical computer uses existing, low-cost opto-electronic components, like the photo array on your smartphone. So there is great potential to scale up the processing power, while miniaturising the components involved.
In an exciting new collaboration, Barclays and Microsoft have signed a one-year research agreement to build on Lee’s research into optimising transaction settlement. Microsoft’s Project AIM team has already run what they call a “toy version” of Lee’s problem, and their optical computer solved it with 100% accuracy. Now Lee and the Project AIM team have begun designing larger-scale versions of the problem with more data and greater complexity. They expect to test them on an upgraded version of Project AIM later this year.
Lee says that working with Microsoft’s Project AIM team is a unique opportunity. “It’s very exciting to be involved in something that has the potential to create innovative change,” he says. “To be on the leading edge of what’s possible.”
Optimising transaction settlement is a good test case for Project AIM, and because clearing houses provide this service for their members, the research has the potential to benefit the industry more broadly. It could pave the way for optical computing to tackle other banking problems, such as fraud detection.
The tech challenges that Lee and his team work on are at the very edge of what is possible, collaborating with some of the most exciting partners around. “Operating on the boundary, looking to push the envelope and to contribute to it in a small way – that’s very exciting.”
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