Quantum computing has the potential to transform AI

Research suggests Quantum AI could be one of the four fundamental capabilities that differentiates quantum machines from today’s classical computers

Promising to take computing to a new level, quantum machines represent a new approach that could offer a number of real-world benefits from the automotive to the pharmaceutical industries.

Quantum computing is a rapidly emerging technology that harnesses the laws of quantum mechanics to solve problems that today’s most powerful supercomputers cannot practically solve. 

Capable of solving problems up to 100 million times faster than traditional computers, quantum computing has the potential to comprehensively speed up processes on a monumental scale. Quantum computers use qubits, which can be 1 and 0 simultaneously, allowing these machines to handle much more complex problems.

“Quantum, in terms of importance to business, society and the EY organisation, is akin to what AI represented years ago,” said Andy Baldwin, EY Global Managing Partner – Client Service, this year in an announcement of a quantum strategic alliance with IBM.

Achieving this is likely to take some time: McKinsey estimates that by 2030, only about 5,000 quantum computers will be operational. But when the technology does arrive, McKinsey suggests Quantum AI could be one of the four fundamental capabilities that differentiates quantum machines from today’s classical computers.

“Quantum computers have the potential to work with better algorithms that could transform machine learning across industries as diverse as pharmaceuticals and automotive,” it says. 

In the pharmaceutical industry, McKinsey says, quantum computing has the potential to revolutionise the research and development of molecular structures in the biopharmaceutical industry. With quantum technologies, research and development for drugs will become less reliant on trial and error, and therefore more efficient.

Already, steps have been taken to explore these possibilities. Boehringer Ingelheim entered into a partnership with Google Quantum AI for quantum computing in early 2021, with the agreement focusing on researching and implementing cutting-edge use cases for quantum computing in pharmaceutical R&D, specifically including molecular dynamics simulations.

And earlier this year, biotech company Moderna and IBM announced they would explore next-generation technologies such as quantum computing and AI to advance and accelerate mRNA research and science.

As part of the deal, the two companies said Moderna scientists would learn how quantum technology could be applied to previously intractable problems for classical computers, with an announcement saying the companies would explore the potential application of quantum approaches to Moderna’s scientific challenges.

“IBM’s purpose is to be the catalyst to make the world work better, perfectly exemplified by this partnership with Moderna. We are witnessing a revolution in the world of computing, driven by extraordinary advances in AI and quantum computing,” said Dr Darío Gil, Senior Vice President, and Director of IBM Research.

Quantum computers boosting the performance of machine learning

Research has shown that quantum computers have the potential to boost the performance of machine learning (ML) systems, and may eventually power efforts in fields spanning everything from drug discovery to fraud detection.

Most people will have been inconvenienced at some point by a payment being refused, or may perhaps have fallen victim to a fraudulent transaction. Algorithms used in the payment card industry mean this is, fortunately, a rare occurrence. But, as Richard Hopkins - Distinguished Engineer at IBM and Fellow of the Royal Academy of Engineering - explained to Technology Magazine earlier this year, even small improvements to those algorithms will have a sizable impact, with evidence already demonstrating that quantum computers can help to resolve these common problems.

“We've recently published a paper where we took information about real debit and credit card details and transactions, and passed them through a quantum algorithm and two conventional algorithms,” Hopkins says.

“Even with today's quantum hardware, provided we let the quantum computer select the parameters to predict the fraud, then we would get the same level of accuracy out of an intermediate-scale quantum computer,” he explains. “That, in itself, is not bad going, but it doesn't get you to that quantum advantage.”

However, as Hopkins describes, the quantum algorithm was able to make qualitatively different judgements and, as a result, come to different conclusions.

“When we look at these results more closely, we found that, first of all, the quantum algorithm chose different parameters,” he says. “And then, when we looked at the results again, we saw that it was making qualitatively different judgments. The accuracy was the same, but it was making a judgement on different elements and coming to different conclusions in many cases.”

As Hopkins describes, these hybrid applications will have a number of use cases, particularly in the world of ML.

“I think what you're going to see, especially in the ML space, is that these hybrid algorithms will emerge fairly early on, where you are combining the power of an existing algorithm that can run at high speed with a quantum algorithm, which will run slower, but will actually take a qualitative different decision, using a completely different algorithm than the other one,” he predicts. 

“In the commercial space, you'll see these hybrid algorithms begin to dominate, where you're using both together to come up with something that's better than we could do today on a classical computer or even on a supercomputer.”


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