2025 is the year that quantum computing reached a pivotal moment. Once the domain of theoretical physics and science fiction, it is now transitioning into a practical, if still emerging, technology.
Two of Capgemini’s leading voices – Chief Innovation Officer at Capgemini’s Qlab Julian van Velzen, and our own Head of Quantum Algorithms James Cruise – shared candid insights on the current state of quantum computing, its real-world applications and the challenges that lie ahead.
The pair revealed how their teams work with clients to build the tools and partnerships that are needed to turn the quantum computing dream into tangible business value. Their key message? Quantum computing is now a strategy for growth – and the rewards will be transformative for those willing to engage with its complexities.
Quantum computing has long promised to revolutionise how we solve problems. Unlike classical computers, which process information in binary bits (0s and 1s), quantum computers use qubits – quantum bits that can exist in multiple states simultaneously.
This property, known as superposition, allows quantum computers to explore vast solution spaces. When combined with entanglement and interference, other quantum phenomena, it opens the door to solving problems that are currently intractable for even the most powerful classical supercomputers.
But as Julian van Velzen is quick to point out, quantum computing is not simply a faster version of classical computing. It is a fundamentally different paradigm. James Cruise’s vivid analogy illustrates this: “Imagine needing to cross the Mediterranean Sea from Spain to Morocco. A classical computer is like a high-speed car that drives around the entire coastline. A quantum computer, by contrast, is like a boat that cuts straight across. It’s not necessarily faster in every context, but it takes a radically different – and often more efficient -– route to the solution.”
Both stress that this distinction is crucial. Quantum computing isn’t about replacing classical computers, it’s about augmenting them in specific, high-value scenarios. That hybrid vision is central to Capgemini’s and Cambridge Consultant’s strategy. “We are investing heavily in understanding how quantum computers can work alongside classical infrastructure – CPUs, GPUs and high-performance computing clusters – to solve complex problems. The key is identifying the right tool for the right task,” says Julian.
Quantum computers excel in situations where data is relatively small, but the computational complexity is enormous. But they’re not suited to large-scale data processing or tasks that require rapid data loading. “Anyone who tells you quantum will solve the data dilemma is wrong,” James says. This is a common misconception, and one that Capgemini is actively working to dispel through education and client engagement.
Quantum entanglement: building partnerships
Rather than chasing hype, Capgemini and Cambridge Consultants are focused on building deep technical expertise and forging strategic partnerships. Teams work closely with universities, including Edinburgh, and hardware leaders such as IBM to explore the real-world capabilities and limitations of quantum computers. The goal is to identify the ‘small nuggets’ of quantum advantage – specific calculations where quantum can unlock new value that classical computers can’t.
One example comes from the aerospace sector. Airbus, a long-time Capgemini client, is exploring how quantum computing can help model the corrosion of aluminium. The problem centres on how copper impurities affect oxidation – a complex chemical interaction that is difficult to simulate accurately using classical methods. By improving the precision of these simulations, Airbus can feed better data into digital twins, which in turn inform maintenance schedules and improve aircraft reliability. It’s a carefully designed use case, but one with significant downstream impact.
Another promising area is drug discovery. Capgemini is working with pharmaceutical giant GSK to explore how noisy quantum simulations can generate data for AI models. AI, as James Cruise notes, is a ‘data-hungry beast’. It thrives on large, diverse datasets, but generating that data experimentally is expensive and time-consuming. Quantum simulations, even imperfect ones, could help fill the gap – accelerating the learning process and enabling faster, more targeted drug development.
Simulating molecules and materials is computationally intensive, and quantum computers are uniquely suited to the task. But again, specificity matters. “Finding the ground state of a molecule doesn’t deliver a new drug,” James notes.
Capgemini is working to identify exactly which chemical calculations quantum can accelerate – and how those improvements cascade through larger workflows. One project involves designing new enzymes using AI to predict structure and quantum to calculate catalytic potential. The results feed back into the AI, creating a virtuous cycle of innovation. Work is also underway with TotalEnergies in the field of carbon capture.
What unites these projects is a commitment to practicality. Capgemini doesn’t run proof-of-concept experiments on real quantum hardware just for the sake of it because, as James explains, “the machines aren’t good enough yet”.
Modelling qubits to solve problems
Instead, the focus is on resource estimation – modelling how many qubits and what fidelity levels are needed to solve specific problems. Collaborations are also in place with hardware start-ups like SeeQC who are improving quantum control systems and readout fidelity – technical improvements that have a direct impact on the timeline to value.
One of the most formidable challenges in quantum computing are errors. Unlike classical systems, which are largely self-correcting, quantum computers are inherently noisy. “Classical computing has made us lazy,” James jokes. “In quantum, we have to think like communication engineers.”
That means using techniques like error suppression, correction and mitigation – akin to tightening the string on a tin-can telephone, repeating messages and using context to fill in gaps. Capgemini is developing tools to manage errors across the entire quantum stack, from hardware to application.
Facing the quantum threat
But quantum’s power also poses a threat. It could break today’s encryption. That’s why post-quantum cryptography (PQC) – new mathematical methods resistant to quantum attacks – is a top priority. Julian Van Velzen emphasises that PQC isn’t about quantum computers per se. “It’s a cybersecurity upgrade,” he says. “But it’s urgent. Systems being deployed today – especially in sectors like energy – must remain secure for decades.”
Cambridge Consultants is working with the UK’s National Energy System Operator and the University of Edinburgh to help the energy sector assess and mitigate the quantum risk. Tools have been developed to help classify vulnerabilities, prioritise upgrades and plan orderly transitions – avoiding costly, chaotic overhauls later.
The energy sector faces a unique challenge. While the data it protects may have a short lifespan, the infrastructure itself – transformers, substations, control systems – can remain in operation for 20 years or more. That means decisions made today will determine whether these systems remain secure in a post-quantum world.
Advancing quantum sensing
Quantum sensing is another frontier. Here, devices use quantum effects to measure time, magnetic fields, or molecular traces with extraordinary precision. Applications range from GPS-free navigation to life sciences. Experts at CC and Capgemini are exploring these technologies too, building magnetometers and experimenting with NV-diamond sensors. The goal is to understand how quantum sensing fits into the broader landscape of digital transformation.
It’s important to not be overly prescriptive about where quantum will deliver value. While chemistry and optimisation are often cited as natural fits, James Cruise warns against sweeping generalisations.
“Quantum computing is good for chemistry. Chemistry is good for drug discovery. Therefore, quantum computing is good for drug discovery – that logic doesn’t always hold.”
Instead, the focus should be on identifying specific calculations within those domains where quantum can make a measurable difference. This approach requires a shift in mindset. Rather than pushing quantum technology for its own sake, the focus should be on real-world needs. Key questions to ask are, where are the real challenges; what problems do clients wish they could solve today but can’t; and how might quantum computing, in concert with classical computers and AI, help unlock those solutions?
This philosophy extends to client work across sectors. In banking, for example, the focus is on securing sensitive data against future quantum threats. In energy, it’s about ensuring that long-lived infrastructure remains secure and adaptable. In pharmaceuticals, it’s about accelerating discovery while maintaining scientific rigour. In each case, the emphasis is on integration – on building workflows that combine quantum, classical and AI technologies in ways that are practical, scalable and aligned with business goals.
Teams also invest in internal development. They’re not just analysing other people’s algorithms – they’re building their own. This hands-on approach aids understanding of the real challenges of quantum development, from algorithm design to hardware integration. Explorations are even under way to use multiple quantum computers with different modalities in tandem, asking how to combine their strengths rather than treating them as isolated tools.
As James Cruise puts it: “We’ve moved beyond the ‘if’ to the ‘when.’ The challenge now is to figure out how and where to use quantum – and to stay open to surprises. Quantum won’t replace classical computing. It won’t solve every problem. But used wisely, it could unlock solutions to challenges we once thought impossible.”
The convergence of quantum computing, AI, and classical high-performance computing is creating a new technological landscape. The approach of Capgemini and its deep tech powerhouse Cambridge Consultants – deeply technical, strategically agnostic, and relentlessly practical – offers a model for how to navigate it.
