Seven sectors impacted by quantum computing

As is the case with all new technology, applications that are currently unimaginable will be developed as quantum computing evolves, creating new opportunities. Here is a non-exhaustive list of them.

Artificial intelligence, a technology with incredible potential, is nevertheless confronted with a major problem: the limitation of the computing capacity of conventional computers to execute heavy algorithms.

All of the feedback from AI systems is based on calculations of probabilities for a range of possible choices. Although processing power has increased tenfold over the last 30 years, certain problems appear to be beyond the capabilities of traditional computing.

By increasing the processing potential of AI systems through their combinatory power, quantum computers could reduce the learning and processing times for numerous AI applications, in addition to improving reasoning and understanding.

As a technical solution, quantum computing could help propel AI into a new era in terms of security and the speed at which algorithms are run. This sort of breakthrough could give unprecedented impetus to multiple sectors, such as the Internet of Things, natural language processing and driverless vehicles. The latter require intensive calculations, which become increasingly difficult as data and more complex relations are added to variables.

The majority of passwords for online accounts and secure communications and transactions are currently protected using encryption algorithms. These systems enable Internet users to securely share data, which only someone with the right “key” can access. It currently takes an extremely long time for traditional computers to solve the mathematical problems hidden behind encryption based on properly dimensioned keys.

However, the advent of the quantum computer will make such systems obsolete. A quantum computer will have the potential to break any current encryption system, making it a serious threat to the cybersecurity systems on which almost all companies rely.

As a consequence, a whole new generation of encryption technology will be needed to protect sensitive data from potential attacks carried out by quantum computers. Scientists are already working on this post-quantum cryptography, as they try to prepare for this eventual tipping point.

Although, as we have seen, quantum computing could pose a threat to data security (and, as a result, to financial data), it also opens up new opportunities for the sector in terms of the security of electronic exchanges, assessing risks and detecting fraud. In terms of fraud, we know that financial institutions lose between $10 and $40 billion in revenue each year due to fraud and poor data management practices.

By performing complex, large-scale calculations, a quantum computer would be capable of producing financial forecasts and thus improving our understanding of certain economic phenomena. Quantum computers are also expected to have  greater data-modelling capacities, enabling them to identify models, devise classifications and make predictions that are not currently possible with standard computers given the challenges posed by complex data structures.

One of the biggest breakthroughs that quantum computing is very likely to bring about is the precise modelling of molecular interactions.

At the moment, scientists often need to examine the exact structure of a molecule in order to determine its properties and understand how it may react with other molecules. Given that computers are not capable of simulating basic molecules with relatively few atoms (each atom interacts with other atoms in a complex manner), scientists have to synthesise the molecules in question in order to physically measure their chemical properties. In many cases, the molecule does not function as expected, which requires to additional synthesising and more tests. As a result, developing new chemical products can be an extremely long and costly process.

As they can focus on the existence of 1s and 0s simultaneously, quantum computers could enable the successful mapping of even the most complex molecules. This could open up opportunities in a several fields, from developing drugs and producing fertiliser (which are currently responsible for 2% of global energy consumption) to the elimination of carbon dioxide. These advances could have significant consequences for the energy sector and the environment.

As discussed in the previous section, quantum computers will enable the simulation of increasingly complex molecules, as well as the interactions between different drugs. Each small step made in this direction will speed up the development of new drugs, and could lead to new treatments.

More broadly, doctors will be able to use quantum computers to accelerate their understanding of diseases and to enhance the precision of treatments. In this way, clinicians will be able to incorporate large numbers of inter-functional data sets into their patient risk-factor models, identify targeted treatment protocols more quickly and personalise them, and develop a more detailed understanding of where and how a given protocol has either succeeded or failed.

Weather forecasts are based on vast numbers of variables such as atmospheric pressure, temperature and air density. This makes conventional simulations very time-consuming and means that forecasting becomes difficult beyond a few days.

However, many sectors, such as agriculture, farming, transport and energy production are reliant on accurate weather forecasts in order to optimise their activities, or to take precautions in response to imminent natural disasters.

In all sectors, a large number of complex commercial problems are linked to a multitude of different variables, many of which are difficult to anticipate. To prevent these problems, reduce losses and operate more efficiently, companies have to perform long and costly calculations, which are not always reliable.

With their ability to operate using multiple variables at the same time, quantum computers are expected to enable companies to perform better data analysis and more robust modelling, and to help them optimise their logistics and workflow scheduling associated with their supply chain management.