The prospects for hybrid quantum optimization algorithms in the manufacturing industry are particularly promising.

Various industries are becoming increasingly aware of the potential of quantum technology and the prospects for the manufacturing industry are particularly promising. There are already quantum algorithms being used for specific manufacturing tasks. These are hybrid algorithms that combine quantum calculations and conventional computing—in particular high-performance computing. As the first benefits of quantum technology are already being realized today, it’s worthwhile for companies to familiarize themselves with the technology now.

Where quantum algorithms fit manufacturing

To find suitable use cases, it’s helpful to know about one of the most popular hybrid algorithms: the Quantum Approximate Optimization Algorithm (QAOA). QAOA is considered a variational algorithm and is used to find solutions to optimization problems. A Variational Quantum Algorithm (VQA) is an algorithm based on the variational method, which involves a series of educated guesses performed by a quantum computer refined by classical optimizers until an approximate solution is found. This iterative process combines classic computers with quantum computers, allowing companies to access the benefits of quantum computing more quickly, rather than waiting for technological breakthroughs that may not happen for several years. 

Hybrid quantum algorithms open creative possibilities for challenges in manufacturing. For example, it will be possible to develop new, better materials by simulating the interaction of molecules more reliably and quickly. Classical computers already struggle to simulate simple molecules correctly. Since quantum computers can explore several possible paths simultaneously, they are better able to calculate complex interactions and dependencies. This reduces the cost and time required to research and produce innovative materials – which is particularly promising for the development of better batteries for electric cars.

Quantum calculations can also make a difference in logistics and inventory management, where, the “traveling salesman problem” is a recurring challenge: what is the shortest route to visit a list of locations exactly once and then return to the starting point? When solving this type of problem, quantum computers are significantly faster than traditional systems. Even with just eight locations, a traditional computer needs more than 40,000 steps, where a quantum computer solves in 200 steps. Those who firmly integrate such calculations into work processes will be able to save a lot of time and resources.

The situation is similar for supply chains. Maintaining one’s supply chain despite geopolitical upheavals is increasingly becoming a hurdle for the manufacturing industry. Remaining flexible is easier said than done, as changing suppliers can quickly lead to delays in the workflow. Although most manufacturers have contingency plans and replacement suppliers at the ready, the market is convoluted. Huge amounts of data must be considered to find the cost-optimal and efficient supply chain. Quantum algorithms can handle this and allow ad hoc queries of this kind, which is a decisive advantage in volatile situations.

Approaching the quantum advantage

Hybrid quantum algorithms can be used in a variety of ways. Volkswagen, for example, found a use case in the application of car paint and was able to optimize this process. It was possible to reduce the amount of paint used and speed up the application process at the same time. 

Some practices help manufacturers to enter quantum computing via hybrid quantum algorithms. Although the full quantum advantage will only unfold in the future, awareness of the technology’s potential is important today. Now is the best time to actively engage with quantum computing and identify industry-specific use cases. This makes it possible to estimate the complexity of the problems and the computing power required. This in turn makes it easier to estimate when the right hardware might be available.

Once suitable application scenarios have been found, there is no need to wait for the ideal quantum hardware. Instead, manufacturers should try their hand at a simplified program for a specific scenario and combine the latest quantum technology with conventional systems. At best, this hybrid approach can achieve a proof of concept and realize tangible improvements – Volkswagen is a good example of this.

It’s also important to note that it’s usually not necessary to learn programming for quantum computing at the machine language level. There are already higher-level programming languages that are less abstract and complex and therefore easier to learn. The market also has platforms that represent quantum-based applications via graphical user interfaces. These can help development teams show these applications to other departments and make them easier to understand. It’s advisable to focus on platforms that are cloud-based and agnostic in terms of hardware. It’s currently still unclear which hardware will prevail in quantum computing. Flexibility is therefore particularly valuable to minimize conversion costs, which can be incurred with on-premise installations.

A strategic investment

Even with the most innovative technologies, big changes don’t happen overnight. While we will see leaps towards the full quantum advantage, it will also take time to be fully applicable. The bottom line is that those who have prepared themselves earlier will be able to utilize the quantum advantage sooner. The transition to quantum computing can be a challenge if not enough groundwork has been done. A smooth transition is possible if employees are trained in the use and maintenance of quantum systems.

The introduction of hybrid quantum algorithms is also strategically valuable due to potential patent applications. Only an early discovery of industry-specific quantum applications allows manufacturers to quickly fill their portfolio and legally secure this intellectual property.

Erik Garcell is head of technical marketing at Classiq Technologies, a developer of quantum software. He has a doctorate in physics from the University of Rochester and a master’s in technical entrepreneurship and management from Rochester’s Simon School of Business.