Quantum computing is a powerful tool to efficiently address computationally difficult problems with high classical complexity. McKinsey estimates, that full adoption of quantum computing could contribute to up to astounding 20% reduction in total global emissions. With sustainability market reaching 8.7 bn EUR in 2022 and strong regulatory incentives for sustainable development, corporates should look to exploit this opportunity to improve their own operations, with a long-term view.
As quantum computing is most suited for addressing problems with high computational complexity, the use cases for sustainability stem from computationally difficult problems, which we divide into three categories:
1. Chemistry-like problems
Most experts in quantum computing applied to sustainability see the greatest potential of quantum computing in solving chemistry problems. These are extremely computationally difficult and can often not be modelled on classical computers, due to their intrinsic quantum nature.
Most pressing problems in chemistry, which could have a broad environmental impact are:
- Design of new catalysts
- Design of new materials
- Uncovering new reaction mechanisms
All three categories could have a tectonic impact on the global emission, with McKinsey estimating the largest potential to lie in designing new cements and building materials, followed by design of new batteries and new materials for hydrogen power generation.
Chemistry-like problems show a tremendous potential in mobility, from design of high-performant batteries to new materials for construction and manufacturing of electronic semi-components.
McKinsey estimates that chemistry-like applications could reduce total carbon emissions by 7 gigatons yearly by 2035.
2. Process optimization
A huge area of interest for quantum computing are also computationally difficult problems in process optimization. Large logistics players have invested billions into technology, especially for tacking the travelling salesman problem, reduction of empty miles, and other types of route optimization. Aviation companies are using quantum computing to balance carry, choose optimal routes and optimal flight distributions.
The power for data processing and storage has increased more than 1,000,000 times since 2012. The increased energy usage translates directly into larger carbon emissions.
Other applications of quantum computing to process optimization include ML/AI algorithms, which have progressed exponentially in complexity in the past years, with increasing number of independent parameters needed to satisfy customer requirements. GPT-3, a natural language processing model by Open AI, for example optimizes the model over 175 bn parameters and each training costs approximately 5 mn EUR, with model requiring several runs to function. Quantum computing can be used in large manufacturing plants to optimize not only costs, but also energy efficient of the manufacturing process. The technology has been explored by large players, such as Mercedes Benz, Aisin and others.
3. Incomputable Problems
Incomputable problems are a third key field, where quantum computing is expected to achieve a large environmental impact. These problems include problems with large degree of unpredictability, such as weather forecasting and natural disaster prediction, where already a small improvement in the state-of-the-art could have a far-reaching impact on how we adapt to weather phenomena and remedy natural disasters.
Other currently incomputable problems include fluid dynamics, which would allow for design of more energy efficient cars, trains, airplanes and other vehicles, contributing to reducing carbon emissions, as well as problems in protein design, drug discovery, modelling of natural phenomena etc..
Conclusion
Quantum computing is made to solve computationally difficult problems effectively, utilizing entanglement to significantly reduce computational complexity. The increased efficiency of algorithms allows completely new applications in fields like chemistry, logistics, data processing and predictions. Advancements in theses fields will have a monumental impact on environment and will be key for developing more efficient batteries, new cements and other materials, optimize logistics and develop more efficient algorithms for ML/AI.
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