IBM predicts it will reach “quantum advantage” in the next 12 months, meaning the time at which quantum algorithms can begin to complement traditional computing for widespread benefits.
The technology giant has been working on quantum computing for decades, but in recent years has signaled that quantum computers that can deliver tangible benefits to businesses are just around the corner.
Further, the firm predicts return on investment (ROI) in quantum across select industries within the next three to five years.
For example, HSBC has partnered with IBM to develop a new machine learning (ML) model for trades, with the intention of improving its margin via better predictions of buying and selling prices.
IBM took a year’s worth of HSBC’s data and applied a quantum algorithm for ‘feature selection’, a process that highlights the most relevant aspects of a dataset to improve model performance.
In tests, IBM and HSBC tracked a 34% increase in successful trades.
At a briefing on the breakthroughs, Adam Hammond, business leader at IBM Quantum EMEA, told assembled media that the model is not yet in production and still needs more training.
Nevertheless, it acts as a tangible example of how quantum algorithms could improve business performance. Outside of banking, Hammond said IBM is hoping to apply quantum computing to a wide range of sectors.
His team is working with Moderna on mRNA vaccines, the Wellcome Sanger Institute on genomics, Boeing on surface chemistry, and Bosch on superconducting hardware, as well as organizations in the farming, life sciences, and electric vehicles sectors.
A seismic shift in compute complexity
At the center of IBM’s latest quantum roadmap is Nighthawk, its new quantum processor which can process up to 120 qubits linked together for a 30% overall increase in complexity.
“One of the key factors in getting to quantum advantage and to useful quantum is being able to fit the size of your problem into the capabilities of the machine that's running it,” Hammond explained.
IBM measures quantum performance in ‘operations per circuit’ (OPC), with today’s chips capable of 5,000 OPC. The Nighthawk chip will use these 5,000 operations more efficiently, Hammond explained, providing a boost in the complexity of tasks that Nighthawk can complete.
On a technical level, IBM has unlocked this capability by moving from a chip layout in which each qubit was entangled with two or three others to one where each qubit is reliably entangled with four qubits.
Hammond noted that this new configuration could lead to breakthroughs in chemistry in particular, as its lattice-like structure mirrors the makeup of the chemical world.
By 2028, IBM believes it will be able to reach 15,000 OPC, tackling problems three times larger than today’s. The next year, in 2029, it is targeting full quantum fault tolerance, at which point its quantum systems will be able to produce reliable results.
The company said it will achieve this with its new large-scale Starling quantum computer, powered by its Loon chip, which will represent a seismic leap forward for the quantum sector.
It’s aiming for this system to run at 100 million OPC, the computational equivalent of one quindecillion of the world’s most powerful supercomputers – a one followed by 48 zeroes.
When ITPro asked Hammond to explain what this would mean for the 2030s, he explained that it was incredibly difficult to predict the future.
“Three years ago, the largest machine we had was 27 qubits – you can simulate a 27 qubit machine on your laptop,” he said.
“It's only when you get above 100 qubits that actually you can't even simulate that on the largest supercomputer in the world. In fact, actually, if you network them all together, you wouldn't be able to simulate it.
“So the hardware has made a really dramatic increase in capability in the last three years. Three years ago, I wasn't talking about real use cases with clients. We were talking about doing experimentation. Now we're really actively exploring real use cases in preparation for machines being large enough to run those in production.”
Despite this, Hammond was resolute that classical computers and AI algorithms will continue to play a huge role in the future of computing. This is because quantum computers are best suited to complex, linear equations – and worse at tasks that today’s computers excel at.
“So in the same way a GPU solves matrix algebra, what a quantum computer does is it solves complex sets of linear equations – and it so happens that that's something that classical computers really aren't very good at doing,” he said.
“So optimization tasks actually simulating nature at a sort of a molecular level, is a complex set of linear equations, linear algebra, if you're doing computational fluid dynamics. So I think what will happen is that some of those tasks that we currently use super computers to do today may well get offloaded to quantum processes.
“But that's all quantum machines do. You're never going to have Excel running on a quantum processor – you might have a particular bit of maths in your Excel spreadsheet that a quantum processor can help you with, but they're never going to replace it.”
Going forward, IBM is also looking at how these complex quantum algorithms can be integrated in existing enterprise architectures.
Hammond said in the case of HSBC, it would need to consider how its machine learning model for trading could be made to follow its checks for security, privacy, performance, and resilience.
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