The number of VC-funded quantum startups in Europe is growing rapidly, with many making bold claims that their technology could one day revolutionize the world economy. Yet it remains difficult to answer one fundamental question: what can quantum computers actually do?
For now, the quantum computing industry remains in what is known as the NISQ era – Noisy Intermediate Scale Quantum – where error rates and the limited size of quantum processors significantly limit the power of quantum computers. A full-fledged quantum computer, which would be able to solve complex calculations intractable to today’s supercomputers, is still years away.
But the biggest industries—think finance or pharmaceuticals—are waiting for the unveiling of a large-scale, fault-tolerant quantum computer to figure out what uses they can make of it. Working with quantum companies, many are already identifying use cases that could benefit from quantum computers and developing the algorithms to enable them.
We’ve rounded up the most promising so far.
Quantum in the NISQ era
But first, it is important to explain how quantum is used in our current pre-breakthrough era. Quantum applications usually involve combining the functions of classical and quantum computing into what are known as hybrid algorithms—quantum algorithms where part of the computational workflow is delegated to a classical processor. In another approach, classical algorithms simulate the behavior of a quantum system on a classical processor. These are called quantum-inspired algorithms.
In some cases, these models already show an advantage over purely classical methods – although not a significant one. But the value is in the exploration process.
“The speed at which a company is able to adopt quantum is heavily dependent on testing these methods today,” said Ekaterina Almasque, general partner at VC OpenOcean. “When quantum hardware reaches quantum advantage for a broad set of algorithms, these early wins will allow companies to leapfrog the competition with serious acceleration.”
The ability of quantum technologies to run many calculations at once means that they are particularly well-suited to problems that require simulating scenarios with many different variables or choosing an optimal path from different options. This is relevant for a number of use cases in the financial sector.
The Spanish quantum startup Multiverse Computing, for example, has partnered with the Spanish bank BBVA to improve the optimization of investment portfolios. It is a well-known problem in the financial sector that requires taking into account the impact of many external factors on asset performance. The experiment showed that Multiverse’s quantum-inspired methods accelerated the computational process and were able to maximize profitability while minimizing risk.
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Another use case in finance is option pricing. Swiss startup TerraQuantum is partnering with financial firm Cirdan Capital to use quantum-inspired algorithms to price a complex class of options called “exotic options” — a process usually done with mathematical operations based on market simulations. The startup says initial results have shown a 75% increase in pricing speed compared to traditional methods.
Financial organizations are also looking at quantum computers to improve credit risk analysis. French startup PASQAL is working with Multiverse on a quant approach for French bank Crédit Agricole to better predict credit rating downgrades in borrowers. Classical methods already exist for this problem, but cannot address the particularities of individual situations. The bank expects that quantum algorithms will improve the efficiency of the process.
Drug design requires identifying the right drug target—the protein, DNA, or RNA in the human body that causes a particular disease—and then developing the molecule that will most effectively and safely alter that target. With the almost unlimited number of possible targets and molecules, finding the exact combination is a years-long, expensive process that is still based on trial and error.
Paris-based startup Qubit Pharmaceuticals uses hybrid quantum algorithms to create digital twins for drug molecules. These quantum-based models are able to represent a large number of molecular properties, which means they can simulate how these molecules interact with other components and can predict their behavior with a high degree of precision. This enables researchers to produce and study molecules digitally instead of having to synthesize them. According to the company, the technology could ultimately halve the time it takes to screen and select promising drug candidates and divide the required investment by 10.
Weather forecasts, which rely on simulations based on data points taken from actual weather conditions, are notoriously error-prone. Predicting the weather with more accuracy would require painting an accurate picture of dozens of parameters and assessing how they interact—a model far too large for a standard computer.
The ability of quantum computers to take into account many different parameters could be a game changer. The German chemical company BASF, for example, is using PASQAL’s technology for its weather modeling applications with the aim of achieving quantum advantages over classical approaches in the short term.
Improving battery design means developing a next generation of devices that are more durable, safer and cheaper. Similar to drug design, the key issue is determining the exact set of parameters that will lead to an improved material.
The Finnish quantum startup IQM, which last year raised €128 million. for its climate-focused technology, says that the ability of quantum computers to accurately simulate chemical processes at the atomic level could enable the development of more efficient batteries. This is also the view of London-based Phasecraft, which develops quantum software that addresses battery modeling challenges. According to Phasecraft, quantum computers could model battery materials exponentially faster than standard devices.
Smarter electricity grid
The power grid is a vast network of sensors, communications infrastructure, data management systems, and control mechanisms that must be carefully coordinated and synchronized to deliver electricity to the network—a complex and time-consuming task that quantum computers are well-suited to perform more efficiently.
Multiverse has partnered with the Spanish utility company Iberdrola to identify how quantum algorithms can optimize the management of the electricity grid. The project focuses on different use cases that require the evaluation of many different combinations. For example, the company hopes that quantum algorithms can help determine optimal battery locations within the electrical network.
There are many factors that can affect the time it takes to get from point A to point B. Quantum algorithms are being developed to calculate how any possible route might be affected by any possible factor, in order to determine the most optimal way.
The French startup Quandela, for example, is working with the multinational Thales to build a quantum algorithm that can optimize drone traffic. As the number of drones flying in urban areas increases, Thales expects that classic computers will soon be unable to account for all the parameters that affect the trajectory. These range from the drones’ mechanical flight limitations to avoiding collisions with other drones, through taking into account areas where they are prohibited and preserving battery life. Quantum algorithms could model all of these factors to determine the optimal path for each drone.
Predicting and detecting defective parts in production lines has significant economic value for manufacturing, but remains difficult due to the overwhelming amount of data that must be considered in order to make such predictions. Multiverse and Bosch are developing quantum algorithms to create digital twins that simulate the factory line to predict where supply chains will fail and optimize when and where maintenance is needed. Similarly, PASQAL and BMW have teamed up to use quantum algorithms that can simulate the formation of metallic pieces, with the aim of identifying defects and ensuring that parts conform to specifications.
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