Quantum computing is a new and powerful tool that is being explored for its potential to help address some of the world's most pressing challenges, including sustainability and climate change.
Unlike classical computers, which use bits to store and process information, quantum computers use qubits, which can exist in multiple states simultaneously, allowing them to perform certain types of calculations much faster and more efficiently than classical computers. This makes quantum computing well-suited for solving complex optimization problems and simulating chemical reactions, among other applications.
One key application of quantum computing in the energy transition is optimization, which can help to improve the efficiency and reliability of the electric grid, reduce energy waste, and lower costs. Quantum computing can also be used to simulate chemical reactions, which is critical for developing new catalysts and sorbents for carbon capture and removal. Additionally, quantum computing can help to design more efficient batteries and other energy storage systems, which are essential for integrating renewable energy sources into the grid.
Other areas where quantum computing can have significant benefits to sustainability and resilience include:
1) Climate modeling and forecasting, which can help to improve our understanding of climate patterns and predict the impacts of climate change more accurately
2) Supply chain optimization, which can help to reduce waste and improve efficiency in the production and distribution of goods
3) Water resource management, which can benefit from optimization algorithms to manage water distribution and minimize waste
4) Optimization of transportation networks, which can help to reduce emissions and congestion on roads and highways
Quantum computing has the potential to revolutionize the energy industry in many ways, from optimizing the placement and routing of wind turbines and solar panels to developing new materials for energy storage and transmission.
Renewable Energy Grid Optimization: One example of how quantum computing can help with renewable energy is by optimizing the electric grid. In 2019, researchers from Volkswagen used a quantum annealer to optimize the routing of electric vehicles to charging stations powered by renewable energy sources. This led to a significant reduction in the total distance traveled by the vehicles, as well as a reduction in CO2 emissions.
A second example is Mitsubishi Heavy Industries, which has used quantum computing to optimize the placement of wind turbines in offshore wind farms, taking into account a range of factors such as wind speed, sea conditions, and bird migration patterns. This has resulted in significant cost savings and improved energy production.
A third example is E.ON, an energy company that uses quantum computing to optimize the management of its power grid, reducing the need for expensive infrastructure upgrades and improving the efficiency of the grid. This has the potential to significantly reduce carbon emissions from energy production.
Quantum Chemistry for Catalysts: Quantum computing can also help in the design of new catalysts for use in renewable energy systems. In 2020, researchers from IBM and Daimler AG used quantum computing to design a new, more efficient and sustainable catalyst for fuel cells that convert hydrogen into electricity. This new catalyst design could lead to reduced costs and emissions associated with hydrogen fuel cells.
Carbon Capture and Storage: Quantum computing can be applied to monitor and verify carbon storage, such as in forests. Researchers from the University of Waterloo in Canada have used quantum computing to model carbon sequestration in a forest. The models were able to accurately predict the amount of carbon that would be stored in the forest, which can help with monitoring and verification of carbon capture projects.
Optimizing Carbon Capture Sorbents: Another example of quantum computing being applied to carbon capture is in the optimization of sorbent materials used to capture CO2 emissions. Researchers from Harvard University used quantum computing to optimize the molecular structure of a new sorbent material that can capture CO2 with high efficiency and selectivity. This could lead to reduced costs and improved performance in carbon capture systems.
These examples demonstrate the potential benefits of using quantum computing in the areas of renewable energy and carbon capture, including reduced costs, improved efficiency, and reduced emissions.
We follow a seven step process involving data collection, processing, and analysis, followed by building out a network-based representation of the optimization problem to be solved, then determining whether the number of entities, connections, and variables in the network is small enough that it can be done efficiently enough on classical computers. If not, then quantum becomes necessary, because solutions to problems are needed today, not in a million years from now. In our process, we must:
1. Define the target.
2. Refresh available geospatial data.
3. Identify and cluster companies
4. Calculate the amount of change to be on target
5. Network design and scaling
6. Decide if quantum optimization is needed
7. Perform the optimization
The solution: a list of coordinates in both space and time, informing us of where the highest value, highest impact opportunities are that we should source for our project developers first, followed by opportunities that may not be of the same high value, but by being more numerous, revenue generation will be certain to continue. We then reach out to the owners of the properties, providing them with information regarding the opportunity at hand and the plan for making use of it. When they are ready, we can then connect them with our project developers.