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

Quemix will join Quantinuum’s Startup Partner Program. This partnership grants free access to Quantinuum’s high-fidelity quantum computer, allowing Quemix to accelerate FTQC algorithm R&D and jointly promote the real-world adoption of quantum computing.

A paper by Homma et al. from Quemix has been published in Physical Review B. This study proposes a parameter-free DFT-1/2 method that eliminates the need for additional self-consistent calculations, enabling efficient and accurate evaluation of semiconductor band gaps and defect levels.

Quemix researchers propose AIQT, a quantum-native framework for transform learning that interpolates between quantum transformations with minimal parameters. The approach extends the core idea of General Transform (GT) to quantum circuits.

Honda R&D Co., Ltd. and Quemix Inc. have jointly developed a new, world’s first quantum state readout technology.

Gekko et al. from Quemix have published a paper proposing a novel transformation method, the General Transform (GT), designed to enhance the representational power of machine learning models. The paper introduces a framework that replaces traditional fixed transforms—such as the discrete Fourier transform—with a learnable combination of transforms that adapts to both the dataset and the task. The weights of the transforms are optimized jointly with the model, enabling genera

Quemix has raised 550 million yen in Series B funding from investors including SCSK and Mizuho Capital to accelerate R&D for real-world quan

https://journals.jps.jp/doi/10.7566/JPSJ.94.044802 A paper by Quemix has been published in the Journal of the Physical Society of Japan ....

A paper by Huang Xinchi et al. of Quemix has been published in Quantum Information Processing.

【Press Release】Quantum-Supercomputer Hybrid Computing Predicts Qubits in Aluminum Nitride Crystals.

Quemix's proposal has been accepted by the JST's K Program

【PRESS RELEASE】International Conference on Quantum Computing "Q2B 2025 Tokyo" to be Held Quemix to Participate in Q2B 2025 Tokyo as Platinum

https://arxiv.org/abs/2501.07211 Kosugi, Huang, Nishi, and Matsushita present an innovative approach that dramatically improves the...

A paper by Taichi Kosugi and colleagues from Quemix has been published in Physical Review A. In this study, a novel method is proposed for

November 20, 2024 *This is a translation of the press release issued on November 18, 2024.* Quemix Inc. Quemix Inc. (President and CEO:...

Quemix researchers have developed a new quantum algorithm for solving the linear advection-diffusion equation.

Featured in PhysRevApplied: This research introduces a novel AI model that designs efficient quantum circuits for data encoding. Using a tra

FMQAを適用し、アニーリングマシンで任意のポテンシャルを使った結晶構造探索、最適な原子配列を効率的に発見する可能性。