QML for greener energy
In the energy industry, biomass plants have long been recognized as a promising source in terms of renewable energy. However, the production of energy from biomass can also result in significant emissions of carbon dioxide (CO2).Predicting these CO2 emissions is a critical business challenge for energy companies today: For example, international energy provider Uniper explored this computing challenge with the QMware quantum cloud. Here, a hybrid QML model has been applied. With QMware’s hybrid architecture, the QMware platform was just the right fit for the challenge.
The QMware cloud platform serves customers in the energy industry to analyze plant data and sensor measurements to predict emissions and peaks. QML can enhance these predictions. By leveraging quantum computing, QML can process large amounts of data quickly and accurately, leading to more precise emissions forecasting and optimization of the biomass plant processes. The benefits of QML are manifold. Improved efficiency and reduced emissions mean significant cost savings for plant operators, while the use of renewable energy sources could contribute to reducing carbon footprints. Furthermore, by providing more accurate emissions predictions and enabling process optimization, QML can help to reduce environmental impact, increase efficiency, and ultimately, create a more sustainable future.
QMware AG is well-positioned to help organizations take advantage of the power of QML. Its quantum cloud computing services provide businesses with the infrastructure they need to build and deploy their QML algorithms. Additionally, the QMware hybrid cloud architecture allows corporates to easily integrate quantum machine learning into their existing systems. This way, QMware cloud provides just the right backend to tap into this potential for next-level computing power.
Contact the QMware team today to learn how we can help you achieve better results with your machine-learning challenge.
Explore our research papers
Get insights to QMware's hybrid quantum computing approach.See publications