How to ensure quantum algorithms are purpose-built for business value creation

by James Cruise | Jul 10, 2024 | Aerospace, Energy, Life sciences, Semiconductor

We’re approaching a watershed moment for quantum technology – we’ll soon see a quantum computer that can be used for commercial applications. I’ve said before that business leaders mustn’t prevaricate if they want to seize the extraordinary commercial advantage that awaits, but now I’m ready to be even more specific. It’s time to take practical first steps in understanding how quantum technology is different, what it is good at, and where it can be applied within existing systems.

If you need a reference point for these seemingly formidable tasks, I’d recommend Capgemini’s new report on ‘Seizing the commercial value of quantum technology’. As a member of the Capgemini Quantum Lab family, I was delighted to make key contributions to the paper from the very real and very practical perspective of algorithm development.

What do I mean by ‘real’ and ‘practical’? Well, I think it’s more than fair to say that the burgeoning quantum industry – and wider business community – talks a lot about the generalities of quantum computing. I and my colleagues, both at CC and within the Capgemini Quantum Lab, want to move the conversation on to the specifics and practicalities of optimising quantum innovation – and the value that will be derived from it.

The bit I’m describing here is what we refer to in the report as ‘Application Aware Algorithms’ (or Triple A for short). It’s all about maximising the return on quantum hardware investments and it sits alongside two other core capabilities in Quantum Lab’s three-dimensional approach to value realisation. The first is ‘Quantum Advisory’ – helping our clients to define both business case development and adoption strategy – while the second is more of a mouthful. It’s called ‘Quantum-Centric Data-Driven R&D’, and although it might sound slightly convoluted, it’s simply about identifying and executing strategic use cases.

The mathematics of quantum algorithms

When it comes to quantum algorithms, I could talk at length here about the mathematics at play, from linear algebra, asymptotic analysis to statistical inference and much more besides. If you’d like to dive into the detail, please do reach out to me for a chat. But for the purposes of a general business readership, let me keep to my top line message, which is about optimising quantum algorithm by understanding the broader application context.

Developers are currently facing something of a dilemma – the perfect quantum hardware doesn’t exist and will never exist. But this problem can be mitigated with a new approach to algorithm design that embraces two guiding principles:

  • Algorithms should share responsibility for error correction with hardware to help optimise them against hardware characteristics.
  • Algorithms should incorporate application-specific information which will make them more robust to errors and maximise hardware performance.

We believe that this approach will enable ambitious early adopters to gain value from quantum investments as technology matures – thus realising the full commercial potential over time. Quantum Lab’s Triple A algorithms follow this process and are ‘purpose-built for value creation’. What that means in practice is that in conjunction with our clients, we can develop and fine-tune algorithms – and optimise limited quantum resources – to unlock value sooner.

The Capgemini report expounds on another of my key themes – that business use cases will only emerge through informed exploration with the help and guidance of an expert partner. Central to drawing value from these use cases is identifying clear and specific areas where quantum algorithms can outperform classical approaches. That’s also true when it comes to activities that would be impossible through traditional processes.

In the report, we explore three chemistry-based use cases where quantum technology shows promise in terms of delivering practical value, drug discovery, carbon capture and battery technology. More specifically, we are collaborating with Airbus to explore how quantum computing applications might solve complex aerospace challenges.

Jasper Krauser, central quantum co-ordinator for Airbus puts it this way: “The range of use cases is broad and includes challenges in chemistry. For example, modelling reactions in fuel cells might accelerate solutions for hydrogen-powered aviation, a key element towards decarbonisation of aviation.”

It probably goes without saying that my role within the Capgemini Quantum Lab initiative is hugely stimulating – and valuable for all parties. Along with my CC colleagues, it’s great to be able to bring very specific deep tech expertise to the wider Capgemini offering.

Cutting-edge quantum computing expertise

The whole is greater than the sum of its parts as the old saying goes – and in this case the whole is made up of a group of quantum experts with broad experience across different domains and different cutting-edge applied research activities. The pragmatic element is balancing the desire to push the art of the possible with the ability to prioritise value creation from current capabilities.

Our role is to help companies develop use cases with tangible outcomes. It’s about real-world applications and true competitive advantage. In conclusion, our advice is to act now by beginning to integrate quantum workflows into existing business processes. That includes leveraging quantum algorithm capabilities in applications to maximise the potential of near-term hardware. That’s the way to create transformative business value. Drop me a line if you’d like to discuss more on this, I’d be happy to continue the conversation.

Expert authors

Head of Quantum Algorithms | View profile

James leads a team developing quantum computing capability to support future client needs, also creating demonstrators showcasing both the future potential of quantum computing and challenges in realising its potential.

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