Home
Technical Overview
Target Industry/Users
Industries in the following areas who are looking for the breakthorough by quantum technologies to overcome computational complexity in various business problems.
- Material science (Catalysts, Semiconductors, Next-Generation Solar Cells, Computational Chemistry)
- Drug discovery
- Time Series Forecasting (Energy, Healthcare, Finance)
Challenges in Target Industry and Operations
There are various types of business problems which are difficult to solve by current computing systems because of the computational complexity. One of the examples of this kind of problems in the material science is to identify catalyst candidate substances. While it is quite essential in this problem to analyze the interactions between candidate catalysts and reactants, current computers have difficulties in modeling complex catalyst surfaces and comprehensive simulation of molecular adsorption onto surfaces. Therefore, the current approach involves physical experiments in prototyping materials, requiring enormous time and cost.
Technical Challenges
In computational chemistry tasks like catalyst discovery, computational complexity grows exponentially relative to problem size, making certain problems intractable on conventional computers. For such problems, quantum technologies are expected to achieve significant advantages over the current computing approaches
Value provided by Quantum-AI Hybrid Application
We have developed a qantum-AI hybird framework to work on problems like computational chemistry by integrating quantum algorithms with optimization (AI) techniques into business workflows. For example, in the case of catalyst search problems, we utilize the quantum Fourier transform —one of the quantum algorithms to be expected to achieve quantum acceleration— to model the patterns of molecules exposed on the catalyst surface. The adsorption energy calculations for molecules interacting with that catalyst surface are then computed using combinatorial optimization algorithms. This approach is expected to enhance the efficiency of future quantum computer-based searches for catalysts having more complex structures that are difficult to analyze using current computers.
Fujitsu's Technological Advantage
Fujitsu possesses a broad range of advanced technologies, including quantum computing, HPC, machine learning, and combinatorial optimization. We are also advancing the application of these technologies across various business domains. Our technical advantage lies in our ability to develop schemes that coordinate and control these technologies, executing complex workflows with appropriate computational methods to solve real-world problems.
Use Cases
-
End Users
- In fields like materials science, where simulation and modeling of materials are limited within small scale because of the computational complexity, the scientists need to conduct time-consuming and burdensome physical experiments with actual materials. With the use of quantum computers and quantum algorithms enabling the modeling of materials, it is expected to dramatically accelerate material discovery tasks.
-
Application Developers
- By combining various quantum and classical algorithms it is expected to realize practical applications which can solve complex practical problems efficiently by making use of quantum technologies.
Case studies
- A catalyst discovery application combining quantum simulators and AI optimization enabling catalyst surfaces modeling and simulation of molecule adsorption onto those catalyst surfaces. Examples: CeO₂ catalysts (La-doped) and PdZn catalysts.