Quantum and quantum inspired computing are allowing specific tasks to be performed in ways previously considered impossible, and the impact is being seen across multiple areas from finance to life science and beyond.
Dr Ben Wahab Head of Science, Drug Discovery at Fujitsu UVVANCE Life Sciences (pictured) uses multiple advanced computing technologies to deliver improvements to the way in which medicines are designed. Here he explains what a chemist can expect from quantum today, and what the future holds.
Beginning the journey with quantum computing
Quantum computers use the principles of quantum mechanics to tackle certain classes of problems that are not possible or computationally prohibitive for classical computers. And whilst there is the potential for a quantum computer to be adapted to everything a classical CPU-based computer can, in most use-cases, there would be no point; the CPU would win on price, efficiency and general accessibility.
There are over 6 trillion classical compute devices on the planet, from watches to smartphones to desktop and laptop computers. But at a best guess, there are likely less than a couple of hundred quantum computers in the world today. Most of these remain experimental systems, with only very limited deployment for specialised applications. They are very expensive, highly specialised instruments and require users to frame their tasks in a specific way. Because of this, quantum computers are not likely to replace conventional computing but rather sit alongside them and GPUs as an additional compute capability.
Unlike buying PCs or a few GPUs for AI-based work quantum, for most organisations, is a huge multi-million-dollar investment, and so a company tends to know specifically what advantage it can get by using one for a specific task. To this end, at Fujitsu, we consider quantum part of a journey which has three elements.
The first step is the hardware. Fujitsu is among a small number of companies in the world that design and supply quantum computing systems to governments, research institutions and other organisations exploring the technology.
The second is quantum algorithms. Organisations should understand the value quantum computing can deliver before investing. This means identifying suitable use cases, developing quantum algorithms, and understanding where a genuine advantage may exist. For Fujitsu this has led to the development of specialist teams dedicated to helping organisations break complex challenges into candidate use cases, building proofs of concept and test scenarios to assess future quantum opportunities.
The third step is “quantum inspired technologies.” By this we mean that while recognising the development of quantum capability is a journey, can advantages still be gained at the present time? Many organisations ask whether they can realise benefits before large-scale quantum computers become commercially viable. These quantum-inspired approaches allow businesses to begin exploring new ways of solving problems today, using technologies that have been informed by quantum principles while running on available computing infrastructure.
One real-world scientific application of this is our QIMERA high resolution 3D molecular search tool that can search ultra large libraries in a matter of hours, rather than months, which in terms of compute costs is less than a cup of coffee. We have a tag line in the QIMERA team: “designed for tomorrow, delivered today” – that is in essence what quantum inspired technologies are all about.
There are challenges to overcome
Quantum computing is still a nascent technology, with the existing quantum computers being found in areas of active research and not yet capable of addressing many large-scale commercial problems at the level required for routine industrial deployment. However, we can demonstrate the type of problems that could be solved with future machines.
There are commercial offerings that allow teams to explore simple problems and get ready for quantum – in fact several large pharma and materials organisations are already running these in partnerships. Those who will benefit the most, and the quickest, are those that are attempting to understand the capabilities now.
The challenge is scale – we can make hardware of various “flavours” but scaling it from 1000 Qubits (the unit of quantum compute) to a meaningful 10k+ Qubit machine is not a linear technological development. From cooling to synchronous stability and all the allied technologies that surround QPUs (the quantum chip itself) in order to make it work, these are some of the challenges to be tackled.
In life sciences, there is no question that quantum computing will add significant value to several areas such as catalyst and materials design and drug discovery. In chemistry there are specific use cases, such as searching an ultra large combinatorial space where there are simply too many variables to compute in an enumerate-then-evaluate approach. Or anything that requires high accuracy calculation of quantum mechanical properties – this clearly has significant impact in materials and catalyst design.
But as with any tool, knowing how to use it matters. So before bringing in quantum as a tool one needs to decide if the problem is suitable for quantum, what are the constraints and how is the problem encoded. This often requires a quantum computing specialist to help write the problems alongside your internal domain specialists to get an accurate problem setting. The way to win is to get involved in the path to quantum now.
Quantum computing is accessible
With the parameters that I have described, one may ask if quantum computing is a tool that can be used by an academic research team or a start-up now? The answer is yes. There are frameworks whereby a company or a government owns a quantum computer and allows academics or small companies to use it. This is a well-trodden model in science, such as Diamond beam-lines for X-Ray crystallography. For the UK specifically there is in place a national program called SparQ, which as part of the UK Research and Innovation (UKRI) National Quantum Computing Centre aims to facilitate the uptake of quantum.
Uptake will very much rely on price-points and on usage policy – so let’s see how the UK, and indeed policy makers around the world, will respond to what will only be growing demand for quantum computing.
Getting the regulation right
I mention policy makers, who are generally running to keep up with emerging technologies, their use and impact. Quantum computing has the potential to do a lot of very high value tasks, from defence, finance and potentially change how we deal with encryption, blockchain etc. There needs to be regulation to ensure that these capabilities are not available to bad actors. We know that regulation in AI was almost an afterthought, as we are seeing in the news. There needs to be a better way to ensure that proper international cooperation and oversight is part of the development and roll out of any new technology.
Again, the UK is making some headway in this area with the Quantum Standards Network. Launched in June this year, it has several tasks including supporting development of appropriate regulation. But on a global scale we need overarching standards and alliances when it comes to the regulation of quantum and its application. We are already seeing tight export controls from the US and Japan of quantum computers. It is likely such measures will remain in place, along with new standards across the technology stack.
Will quantum have a climate impact?
It is worth noting that the quantum chip (QPU) requires very little energy to run. With the whole device is kept near to absolute zero (-273 degrees Celsius) the chassis in which the QPU sits, known as the “chandelier”, and the associated hardware does take some power to keep it cool, therefore most of the cost is refrigeration.
For reference, a 256Qbit super conducting computer uses about 20kWh-1, that’s about £6 per hour based on UK domestic power. But a machine of this size is quite basic. For the kind of outputs that are going to make a meaningful difference we would want a machine that is nearer 10K Qbits, or 40 times the size. Therefore, taking a straightforward multiple, we could expect energy costs of some £240 per hour, again based on UK domestic power costs. It should be noted that commercial electricity costs are cheaper. But added to this are the costs of the refrigerant gasses, specialist building constraints, skilled technical support etc, at which point the numbers can become somewhat opaque.
With all that being said, it is possible for a quantum computer to achieve in one hour what a classical computer might take to years to achieve. On that basis I would argue that quantum computing should be more energy efficient than classical and definitely better on energy than AI.
There are caveats, as we don’t know exactly what the final systems will look like. But we can estimate the costs, and they are likely outweighed by the net positive outcomes of the compute.
Trusting the results
With investment and regulation in place, can we truly rely on the outputs from quantum computing? This question is true of any computational method. I would say that the question one needs to ask is: “Is it more reliable than other methods?” And this is the tricky bit, as many of the challenges solvable in quantum computing simply can’t be computed in other ways to confirm the results. Validation is unique to each specific use case. But in the end the real validation comes down to the final experimental read out, and this is true from classical compute to AI/ML and beyond.
But to take a micro view on this, one can argue that quantum computing for atomic challenges, for example in the design of new materials, has the potential to be more accurate than classical methods.
Combining technologies to get the right results
Will a quantum computer be coming to your laboratory any time soon? My answer is “not very likely”, for the all the reasons that have been shared above. I think it is more likely that in the future a scientist will set up a group of tasks that are sent to an “Advanced Compute” system and a middleware layer broker will identify the type of task and route workloads to the most appropriate computing source, whether CPU, GPU or QPU. We consider this mixture of CPU, GPU and QPUs all working together under the umbrella of "Advanced Compute” the future. The right technologies at the right time, to get the right result.
Get involved
SCI's Data, Digitalisation and AI Group will be holding an event: From Vision to Practice: Data, Digitalisation & AI in Industry 2 March - 3 March 2027, in London: Find out more here
Further reading
-
Quantum technology is reaching a commercial tipping point for chemicals and pharma
- Why quantum computing matters to chemical companies
- Quantum batteries are even stranger than you might expect
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