Defence Panel Discusses How to Bring Quantum & AI to Bear in a Hostile World

Insider Brief

• Senior defense leaders and technologists at The City Quantum & AI Summit in London said quantum and artificial intelligence will be decisive in maintaining NATO’s strategic advantage amid rising geopolitical tensions.

• Panelists including representatives from Multiverse Computing, Aquark Technologies, BAE Systems, and MBDA highlighted how quantum sensing, AI assurance, and collaboration between startups and defense contractors are reshaping deterrence and resilience.

• Speakers warned that Europe’s quantum ambitions are constrained by funding and regulation, emphasizing the need for faster innovation, stronger public-private partnerships, and sustained defense investment before it is too late.

At a time when technology defines national power, senior defense leaders and technologists say quantum and artificial intelligence could prove decisive in maintaining strategic advantage across the NATO alliance. 

Speaking at The City Quantum & AI Summit’s defense panel discussion chaired by General Sir Patrick Sanders, the high-level defence panel explored how quantum sensing, AI assurance and public-private collaboration are reshaping deterrence and resilience in an increasingly hostile world.The summit, one of the premier summits bringing together Quantum, Defense and the Financial Sector, was celebrating its Fifth Anniversary in London.

General Sanders, who led the discussion,  framed the debate with a historical perspective. Drawing on thinkers from Gramsci to Kissinger, he warned that nations and institutions risk “a strange defeat” when they fail to adapt to technological and ideological shifts. He argued that defense organizations must avoid this fate by embracing innovation at speed:

To that end, Sanders convened four leaders working at the frontiers of quantum and AI transformation: Enrique Lizaso of Multiverse Computing, Andrei Dragomir of Aquark Technologies, Rob Flanders of BAE Systems, and Edwin Bowden-Peters of MBDA.

A Dual Challenge: Competing and Securing

Rob Flanders, Head of Threat and Incident Response at BAE Systems, described a defense environment shaped by what he called a “dipolar threat landscape.” He pointed to active conflict on NATO’s borders and long-term strategic competition with China as defining pressures.

“So we’re faced with a literal war on the border, combined with a much longer term geostrategic threat that essentially sits behind lots of the technologies that we’re looking at, discussing and adopting today,” said Flanders. “So I think it’s from our perspective, very much a dipolar threat landscape that we need to be aware of and be concerned about.”

Flanders said that emerging capabilities like AI-assisted quantum systems demand rigorous assurance before being fielded. “In defense, high assurance isn’t optional,” he added, explaining that engineers must know the provenance of data that trains AI systems.

 “If you put garbage in, you might well get garbage out – and that’s the last thing you want on a strategic program,” Flanders added.

Quantum Sensing and the Return of Deterrence

For Andrei Dragomir, founder and CEO of Aquark Technologies, quantum sensing is already transforming how NATO nations approach navigation, timing, and infrastructure resilience. His company, the first quantum investment of the NATO Innovation Fund, builds compact cold-atom clocks and sensors that work in GPS-denied environments.

Dragomir said that the key advantage of quantum sensing is its ability to “collect good data rather than bad data” – eliminating noise at the source rather than filtering it afterward. That accuracy, he argued, underpins both deterrence and resilience.

“So, having that technology and using it in a proper way can definitely be like the next bow and arrow of defense,” said Dragomir. “It’s also an indirect capability, where if you can navigate in GPS denied environments, eventually your enemy is going to stop spoofing your GPS, because it’s pointless, and therefore you can still rely on the existing infrastructure that you would otherwise have relied on today, and I think it’s going to have a huge impact.”

He forecast that deployable quantum navigation systems could begin scaling within 12 to 36 months, emphasizing collaboration as essential to success.

“I think it’s collaboration that is absolutely essential at this point in time,” said Dragomir. “There’s no single player that can build towards a vision like this by themselves. We need to have our doors very, very open.”

Europe’s Quantum Investment Gap

Enrique Lizaso, CEO and co-founder of Multiverse Computing, offered a candid view of Europe’s position in the quantum race. His firm develops hybrid quantum-AI algorithms for banks, defense manufacturers, and governments. Lizaso argued that Europe’s problem is not talent or ideas, but financing and regulation.

“We have the technology, maybe even better,” said Lizaso, adding, “But the way to convert – to transform – that into something which is more than that, by which I mean create companies that are big enough – is a different way. This is a financial problem at the very, very, very core of the situation, particularly in Europe.”

He also pointed to new defense applications of AI model compression, including lightweight multimodal neural networks that can run on small devices and recognize objects in the field. However, he warned that overly restrictive data-use rules could delay such innovations.

Bridging the Gap Between Labs and Battlefields

Edwin Bowden-Peters, UK Technology Watch Lead at MBDA, underscored how innovation is accelerating beyond traditional defense boundaries. “Access to technology has been completely democratized,” he said, noting that tools once confined to military labs are now commonplace in consumer electronics.

MBDA, which has invested directly in Aquark, is testing new models of collaboration with startups. Bowden-Peters recalled that Aquark caught his team’s attention when they demonstrated a ruggedized quantum device that could be carried rather than confined to a lab bench.

“They cared about it being ruggedized and real world and so immediately we knew that they cared about the same things as us,” Bowden-Peters said. “And so then what we did is we funded an experiment, and we said, can we push this further?”

He added that defense must increase its risk appetite for performance — but never at the expense of safety. “If you come second in war, someone doesn’t go home,” he said. “That’s why we have to test to extremes.”

Investing Before It’s Too Late

General Sanders closed with a reminder that defense spending is not an indulgence but an insurance policy. In 1936, he noted, the U.K. spent 2.9% of GDP on defense; by 1939, that figure had risen to 10%, and by 1945, to 50%.

“The sums we’re talking about today  – half a percent, one percent of GDP  – are nothing,” he said. “Prevention is a hell of a lot cheaper than cure.”

Quantum Matters: Quantum & AI – Early Days For A Killer Combination

Guest Post by Karina Robinson

Artificial Intelligence (AI) suffers from two massive blocks: colossal, costly energy use and a transparency deficit. The technology can be technically feasible but is still too expensive for most organisations to consider it. That was the conclusion of a recent paper from MIT Future Tech, which looked at computer vision tasks as an example of an AI-enabled technology. But the MIT group isn’t alone in highlighting the problem.

The computing cost of Deep Learning is exploding. Sam Altman, CEO of OpenAI, made it clear last year that training ever larger Language Learning Models (LLM) is not the way to advance AI, not least because teaching GPT-4, its latest product, cost over $100m, while by 2027 it is estimated that the AI industry could consume as much power as a country the size of the Netherlands.

On the openness front, the models are far too opaque – usable in consumer applications but a legal minefield for companies to consider rolling out. Their tendency to ‘invent’ plausible facts is also not helpful.

Quantum is the route through which AI’s limitations can be lifted.

Two perception issues are delaying the advance. Firstly, there is an impediment to AI/Quantum cooperation based on misapprehensions that quantum is only about hardware – creating a quantum computer with enough power to break current encryption. That is unlikely to happen for several years. It may take up acres of media space, but much more advanced are quantum sensors, some of which are already in the market, while in quantum communication the Chinese are apparently more advanced, and quantum software/quantum-inspired software is advancing at pace. All of these are based on quantum physics and applicable to AI in different ways.

The second issue is the silo mentality of many of the companies involved in these fields, who have separate divisions for AI and Quantum, or only concentrate on one. Nevertheless, more visionary firms are breaking through the barrier.

Scott Faris, CEO of US firm Infleqtion says, “The convergence of AI and Quantum is one of the most powerful combinations that we are starting to unlock. The convergence will have both immediate and long-term implications.”

Karina Robinson is Senior Advisor to Multiverse Computing and Founder of The City Quantum & AI Summit

The firm, which manufactures quantum products and parts, ranging from sensors to computer hardware, counts NASA as one of its clients. Faris points out that quantum-enabled technologies are “quickly demonstrating their utility in addressing the crushing data infrastructure scaling challenges driven by AI. Scaled networks of quantum sensors will create vast new data sets of unparalleled precision and value which will be unlocked by parallel advancements in AI.”

A case in point is CompactifAI, the product launched late last year by Multiverse Computing*. Europe’s largest quantum software and quantum-inspired software firm, which counts Bosch and the Bank of Canada among its clients, uses its technology to compress the data from a Large Language Model (LLM) by up to 70%, thus using much less computing power, and achieve results that are comparable in quality.

“Left on its own, AI is going to burn the world by consuming intolerable levels of energy,” says CEO Enrique Lizaso. His firm, shortlisted as one of three finalists in the European Future Unicorn Award, is using its AI and quantum-inspired capabilities in fields ranging from forecasting weather catastrophes – on the increase with global warming – to helping car manufacturers in the training of their Machine Vision.

This is done on the premises of the industrial site, rather than data processing centres, with faster retraining of the multiple streams of data, reduced processing power requirements and added security.

Lizaso is adamant that the cross over between AI and Quantum is environmentally helpful in other ways. He notes that optimising routes for shipping, for instance, or optimising the amount of fuel tankers need for a journey, cuts back on the carbon footprint of the ship.

Nvidia, best known as the AI chip market leader, is also keen on quantum.

“AI is accelerating quantum computing today. We’re starting to see researchers tap into the mature infrastructure of AI and accelerated computing to leverage things like Large Language Models (LLMs) to develop new quantum algorithms and improve the performance of quantum computers,” says Tim Costa, who leads the HPC and Quantum Computing Product Team.

“We are just starting to scratch the surface of how Gen AI can improve quantum computing,” he adds, noting however, that in the near term researchers are already investigating quantum machine learning (QML) and quantum-inspired methods for financial applications like fraud detection and forecasting.

Recently, researchers from the University of Toronto, St. Jude Children’s Research Hospital and NVIDIA developed a new quantum algorithm called the “GPT-Quantum Eigensolver.” It uses the framework of generative AI models to generate quantum circuits with desirable properties, in this case to calculate the ground state energy of molecules of interest. Versions of this generative quantum algorithm can be applied towards important problems in drug and new materials discovery, as well as a host of other applications, some of which we cannot even imagine.

As for the problem with the unclear thinking process of AI, and its fantasising, quantum is also part of the answer.  To use it more widely, the interpretability of the system is key – in essence understanding why a system makes the decisions it does so it can be held accountable. Only last week Quantinuum, formed from the merger of Cambridge Quantum and Honeywell Quantum, announced a first public step in creating AI that is “interpretable and accountable” via the development of a framework for compositional models of AI using a type of maths called category theory. Their academic paper has yet to be peer reviewed.

Humankind’s biggest challenges will not be solved tomorrow by the combination of Quantum & AI. Nevertheless, at the risk of creating a hostage to fortune, I would predict many will be solved in the next decades by those firms at the forefront of the Quantum & AI journey.

*Karina Robinson is Senior Advisor to Multiverse Computing and Founder of The City Quantum & AI Summit which takes place on Monday, October 7th.