What is being developed with quantum computers today?

The quantum advantage simplified

Quantum computers use quantum bits instead of classical bits. Their special quantum properties allow them to represent both a ‘1’ and a ‘0’ at once in superposition and work together in an entangled group. Without understanding the physics behind this and how it works, what matters most from an end-user perspective is its impact on computational capabilities. In a classical machine, N number of bits can represent N number of states. By contrast, in a gate-based quantum computer N number of qubits can represent 2N number of states. Or, as put by Sir Peter Knight at the New Scientist Emerging Technology Summit 2024, just 300 good qubits could represent more states than there are atoms in the visible universe. This not only exponentially reduces the time it can take to solve certain existing problems but also provides a means to tackle harder and more complex ones.

For now, classical supercomputers have trillions of classical bits, and many quantum computers available only have a handful of useful qubits. Yet as worldwide efforts are concentrated on scaling up the hardware, end-users are already developing real-world use-cases. This trend can be found across multiple industry verticals, with many players angling to become early adopters and realise a competitive advantage as soon as it is available.

Material simulation: drugs discovery and battery chemistry

Simulating material properties at the nanoscale, or other words at the atomic level, is incredibly compute-intensive with classical machines. Moreover, some of these simulations have a reputation for not being particularly accurate—for example, predicting that an insulating material will be a conductor.

This has an impact in fields dependent on material discovery and simulation across many industry verticals, which is infamous for being a very time-consuming and costly experimental process. However, in parallel with the interest in materials informatics to solve this challenge are many investigations into the application of quantum computing to significantly accelerate materials discovery timelines.

One important example of this is within drug discovery. Players such as Janssen Pharmaceuticals are investigating how quantum computing can be used to make screening of potential drug candidates more efficient, as well as be applied for molecular simulations. This work can specifically relate to the applications in crystal structure predictions or binding affinity, as well as property predictions, including toxicity.

Material property simulation and target finding are also high-value problems in battery chemistry innovations. While classical approaches to materials informatics based on classical computing can enhance our understanding of battery chemistry, it is likely to remain less efficient than quantum computing at modeling chemical reactions at a molecular or sub-atomic level. Simulations of electron interactions can also provide a more accurate understanding of chemical reactions at anodes and cathodes, such as the degradation-inducing formation of oxides. Quantum computing can thus accelerate the development of higher-performance batteries by performing calculations beyond the capability of their classical counterparts.

Banking and finance: pricing optimisation and fraud detection

The finance sector has been amongst the earliest to engage with quantum technology. This is in part because of the criticality of robust data security in finance and, as such, preparing for the risks posed by quantum computing alongside the integration of quantum communications solutions such as quantum key distribution and post-quantum cryptography.

However, beyond the quantum risks to finance, there are also a host of potential opportunities, specifically in pricing optimisation and fraud detection. With quantum computing, pricing optimisation could consider many more influencing factors than it does today, evaluating the combined impact of currency, location, sustainability, supply chain, geopolitics, and more. As for fraud detection, the capability to analyse banking activity with a quantum computer could make security protocols much more streamlined. The erroneously blocked card upon the purchase of the first holiday coffee would become a thing of the past.

At the 2024 New Scientist Emerging Technology Summit, HSBC outlined their strategy of building dedicated in-house expertise to develop quantum-ready products in these areas – which can be deployed as soon as the hardware is ready.  Yet HSBC are not alone in exploring quantum computing – indeed, there is activity from Goldman Sachs, JP Morgan, Barclays, Mastercard, Citi, and many more. However, some of these companies are choosing to use third parties to build use cases rather than invest in in-house teams. In the current era of a quantum talent shortage, particularly within industry, the role of the expert middleman is looking particularly lucrative.

Automotive and aerospace: fluid dynamics and the paint shop problem

The mega-trends in future mobility are broadly electrification and autonomy. Of course, the applications in battery chemistry simulations are of keen interest to the automotive community – who are amongst the most active in this research area from a commercial side. This is covered in much more depth in the IDTechEx Quantum Computing report. However, outside of material discovery – quantum computing also offers an edge in other areas, such as fluid dynamics and logistics.

Computational fluid dynamics (CFD) simulations are a crucial part of the design process within both automotive and aerospace. However, for very complex scenarios, the hypothesis of industry leaders such as Rolls Royce is than for iterative design of jet engine designs – the efficiency of a quantum solution could be hugely valuable.

By contrast, the application of quantum computing to logistics and operations more widely could be transformative. The multi-car paint shop problem is an example similar to the pricing optimisations within finance, whereby workflow scheduling, which accounts for a higher number of variables, will likely be better solved with a quantum computer. For example, D-wave are already ramping up productions scale deployment of an auto-scheduling product using annealing with partners of the Pattison Food Group.

Market outlook and conclusions

Overall, the general themes of ‘optimisation and complex simulation problems’ come up time and time again as killer applications for quantum computers. Across the pharmaceutical, chemical, healthcare, automotive, finance, aerospace industries, and more – quantum expertise is rising in value.

Even though scepticism remains in some instances as to the likelihood of a universal, large-scale fault-tolerant quantum computer ever being realised as promised, there is even more disagreement as to what timelines value will realistically be realised within each industry.

Yet, for many, the risk of missing out and falling behind is too high not to engage. Looking ahead, IDTechEx anticipates the rising awareness of the risks posed by quantum technology, specifically within quantum communications and data security, will likely become a gateway for more commercial players to begin considering the opportunities quantum computing could bring them. Going forward, potential end-users must tread the line of being neither too cautious nor too enthusiastic in response to ‘quantum hype.’

With so many competing quantum computing technologies across a fragmented landscape, understanding the differences between each approach is essential in identifying realistic opportunities for growth within this exciting industry. IDTechEx’s report “Quantum Computing Market 2024-2044: Technology, Trends, Players, Forecasts” covers the hardware that promises a revolutionary approach to solving the world’s unmet challenges. Drawing on extensive primary and secondary research, including interviews with companies and attendance at multiple conferences, this report provides an in-depth evaluation of the competing quantum computing technologies: superconducting, silicon-spin, photonic, trapped-ion, neutral-atom, topological, diamond-defect, and more.