Quemix has been selected as one of five companies for the “Quantum Startup Accelerator Program” conducted by Q-STAR and Plug and Play Japan.

Research revealing the origin of shallow hole traps induced by the GaOₓ interfacial layer at GaN/SiO₂ interfaces through first-principles calculations has been published in Physical Review Materials.

Quemix and Idemitsu Kosan propose a molecular dynamics method combining orbital-free DFT and a machine-learned interatomic potential to efficiently simulate electric-field-driven ionic transport and changes in interfacial charge states.

Quemix has received an investment from Honda Motor Co., Ltd. Building on this investment, Quemix will apply its advanced quantum algorithm technologies to practical materials development processes and accelerate the development of next-generation battery materials and other advanced materials.

Quemix and Mitsui Kinzoku entered into a capital and business alliance on July 17, 2026, to accelerate R&D toward the real-world implementation of quantum computers and address social issues through materials development. The two companies will continue their efforts toward the practical application of quantum chemistry calculations under a stronger framework.

Quemix’s proposal, “Development of a Materials Simulation Platform Using Quantum Computers and Demonstration of Its Application to Battery Materials,” has been selected for NEDO’s large-scale demonstration program. In collaboration with Tohoku University, Quemix will develop quantum computing software for battery-material simulations and validate its effectiveness.

Collaborative research by Quemix and Sumitomo Rubber Industries on a Fourier space readout method for efficiently recovering functions encoded in quantum states has been published in Quantum Science and Technology. The method may preserve quantum computational speedups during the readout process.

Quemix and Honda R&D have developed the world’s first quantum algorithm capable of exponentially accelerating Density Functional Theory (DFT) calculations on quantum computers. The achievement opens the possibility of applying DFT calculations to extremely large-scale systems that would be difficult to handle using conventional computers.

Quemix and SCSK have developed “POD Readout,” a new technology that addresses the readout, or measurement, bottleneck in quantum computing. By directly extracting essential information from quantum states, the technology reduces the required number of measurements by up to a factor of 1,000 while preserving the speed advantages of quantum computing.

Quemix and Nissan have launched joint research and development of next-generation aerodynamic analysis software utilizing quantum computers. The companies developed a hybrid quantum-classical algorithm capable of handling complex real vehicle geometries and confirmed through simulation that it can reproduce conventional aerodynamic analysis results with high accuracy.

Quemix and Mitsui Kinzoku have developed QAVG, a new technology that improves Quantum Phase Estimation (QPE) for materials calculations on quantum computers. By enabling higher-accuracy and faster DMFT calculations while suppressing increases in computational cost, the technology helps accelerate the practical application of quantum computing to materials simulation.

Quemix, Toyota Motor Corporation, Toyota Central R&D Labs., and the Graduate School of Science, The University of Tokyo conducted a proof-of-concept study on efficient task allocation between classical and quantum computers in quantum chemistry calculations. By combining the Density Matrix Renormalization Group (DMRG) method with Probabilistic Imaginary-Time Evolution (PITE), the collaborators presented practical guidelines for allocating computational resources: pushing clas

Quemix and DENSO have developed core technologies for executing molecular dynamics simulations on quantum computers. The new framework directly evolves distribution functions over time and integrates NVT quantum circuits, demonstrating a proof of concept for chemical-state prediction on quantum computing platforms.

By utilizing silicon vacancies (VSi) in SiC as quantum sensors, the study enables direct, high-spatial-resolution measurement of electric fields inside devices. High electric fields up to approximately 2.3 MV/cm and their spatial mapping are demonstrated.

Research on X-ray absorption spectrum (XAS) calculations using quantum computers has been published in Physical Review Applied. The work was conducted in collaboration with Honda R&D Co., Ltd. and was also presented at Q2B Tokyo 2025.

Amplitude encoding of classical data is one of the practical bottlenecks in quantum computing workflows. This work introduces a new approximate amplitude encoding method using the Adaptive Interpolating Quantum Transform (AIQT). By learning a data-adaptive transform basis, the approach concentrates information into fewer coefficients and achieves up to about a 50% reduction in reconstruction error compared with Fourier-based methods.

Mitsubishi Electric Corporation, Institute of Science Tokyo, University of Tsukuba, and Quemix Corporation have achieved the world’s first elucidation of how hydrogen produces free electrons through interaction with certain defects in silicon. The achievement may improve electron-concentration control in silicon IGBTs and reduce power loss, with potential applicability to UWBG materials.

In collaboration with NGK Insulators, Quemix demonstrated the effectiveness of quantum computing in generating machine learning potentials. The study proposed an improved Equivariant Quantum Neural Network (QNN) and identified key technical challenges. The results are published in Physical Review A.

Published in Physical Review A, this study proposes AIQT—a quantum-native framework that enables efficient and expressive learning of quantum transformations by interpolating between multiple operations with only a few parameters.

Quemix and Sumitomo Rubber Industries have achieved a world-first: an exponential acceleration in solving nonlinear differential equations on a quantum computer. This breakthrough was enabled by a newly developed, practical-level readout method that overcomes one of the biggest barriers to real-world quantum computation. The result marks a major step toward practical quantum computing applications in high-performance materials development and other computationally intensive f