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PQML – Quantum Computing
10. April
Our contribution to reducing CO2
The platform is intended to be made available primarily to public research institutions, universities, and larger corporations, enabling them to network, share research results, and recalibrate machine learning models—previously computed using classical methods—within a quantum computing environment.
Advantages of machine learning models in a quantum computing environment
- Posibilities to calculate and compare 100s of models simultaneously
- Up to 1000 times faster calculation
- More accurate outputs through approximation
Quantum Computing
This technology holds huge potential for research and industry. Quantum algorithms – combined with machine learning algorithms – can be used, for example, to optimize industrial processes and make them much more efficient. This can directly save large amounts of carbon dioxide.
Companies can improve their carbon footprint and thus contribute to greater sustainability. For example, there are initial approaches to optimizing workflows and processes in the quality assurance step in the automotive industry. Optimized processes mean greater efficiency and lower emissions, for example because engines do not have to run as long or as often during testing.
Quantum Machine Learning
Successfully integrating quantum machine learning algorithms into industry faces several challenges.
The hardware required to run quantum algorithms is still relatively underpowered and only sparingly available.
Furthermore, potential use cases are often too abstract and not well understood.
Additionally, the opportunities presented by quantum technology are not yet widely recognized in the industry. Even when they are, decision-makers often struggle to find the right contacts and expertise in this field.
Aims of the project
Creation of a platform and a forum of experts in Austria to accelerate research and development in the field of quantum machine learning.
Development of tools and frameworks for the development of quantum machine learning applications on quantum and hybrid quantum hardware.
Develop a sample application in the field of chemistry and microbiology that demonstrates how to combat climate change using the latest quantum computing and machine learning insights.
Providing a platform-as-a-service infrastructure to implement business applications in quantum ML projects.
Laying the foundation for an active and diverse quantum machine learning community in Austria.
Hybrid Quantum Computing
The key to CO2 reduction
As part of our PQML event, we gained deeper insights into one of the most exciting technologies of the future:
Quantum computing and quantum machine learning – with immense potential for CO₂ reduction and the sustainable transformation of our industry. A big thank you to Jona Boeddinghaus (CEO, Gradient0) and Georg Gesek (CEO, Novarion) for their inspiring contributions! Their expertise clearly demonstrated how hybrid quantum computing and AI can work together to create solutions for the climate crisis.
A special highlight was the video message from Alexander Pröll, State Secretary for Digitalization. He emphasized the societal and economic significance of these technologies. The future emerges where technology and sustainability converge – and this is precisely where PQML takes action.
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