Use Cases

Use Case Development Support for Quantum Computing

We provide use case development support services for the industrial application of quantum computing, offering end-to-end assistance from issue identification and theme selection to mathematical modeling, algorithm design, proof-of-concept (PoC), and evaluation. By appropriately combining quantum and quantum-inspired technologies with existing HPC and AI environments, we focus not only on technical validation but also on practical implementability, helping customers define application themes tailored to their business challenges and advance toward real-world deployment. The application areas shown below are only examples, and we can support challenge assessment and use case development across a wide range of industries and business domains.

Logistics / Transportation

CAE (Computer-Aided Engineering)

AI / Machine Learning

Logistics / Transportation

Planning in the logistics and transportation sector can be formulated as a large-scale combinatorial optimization problem involving complex interdependencies among numerous constraints and performance metrics, including delivery sequencing, vehicle dispatching, loading, hub location, workforce allocation, and traffic control. Quantum computing is attracting attention as a new computational approach for addressing such problems, where conventional computing resources often face a high search burden. We provide end-to-end support, from mathematical optimization modeling of business challenges to validation using quantum and quantum-inspired methods, as well as evaluation with real-world operations in mind, contributing to the realization of more advanced logistics and transportation operations.

CAE (Computer-Aided Engineering)

In the CAE field, improving analysis accuracy, computational speed, and design exploration efficiency is a key priority in large-scale numerical computations such as structural analysis, fluid dynamics analysis, thermal analysis, and vibration analysis. Quantum computing is being explored for its potential in computationally intensive tasks such as solving systems of equations, eigenvalue analysis, inverse problems, and design optimization, and is attracting attention as a technology that could support next-generation simulation platforms. We assess applicability based on the computational structure of the target problem and support use case development for advanced CAE through proof-of-concept projects, including hybrid configurations that combine quantum and classical computing.

AI / Machine Learning

In the fields of AI and machine learning, the potential application of quantum computing is being explored across a wide range of processes, including model training, inference, feature selection, clustering, and hyperparameter optimization. In particular, for problems involving high-dimensional and nonlinear search spaces, the use of quantum algorithms and quantum-inspired techniques is expected to improve computational efficiency and representational capability. We provide structured support from theoretical validation and algorithm design to proof-of-concept execution, while ensuring alignment with existing data and AI infrastructures, helping drive the creation of advanced use cases that can lead to future competitive advantage.