Big News!🎉 We are proud to announce that we have raised $10M in Series B funding, to expand overseas markets further as a japan-based quantum computer software startup. Learn more about this capital and our plan here: https://t.co/sUDJVueiho #QuantumComputing

【New preprint🎉】 We proved that simulating the hardware-efficient ansatzes (HEA), widely used in near-term algorithms, belongs to the class known as BQP-complete. This result strongly suggests that such simulations are classically intractable. https://t.co/olwMyJmxLA

【New preprint🎉】 Our joint work with Fujii Lab (UOsaka) on a new FTQC architecture “MB-FTQC”! Measurement-based approach aiming for large-scale QC on near-term devices, with new schemes like “higher-order zero-level magic state distillation.” https://t.co/uGOCHj8NVx

[New preprint on arXiv 🎉] We have posted a paper proposing a quantum algorithm for nonlinear plasma fluid simulation and its numerical verification. The method yields ~4th-order speedup in time scaling with grid size and polylog space complexity. https://t.co/0jcEGZMyAI

This is a step forward in showing how quantum computing and AI together can accelerate the future of drug discovery.

Why this matters: The chemical space for potential drugs is unimaginably vast. By bringing quantum-inspired generative models into the process, we can more efficiently explore this space and align generated molecules with real-world pharmaceutical requirements.

We present QCA-MolGAN – a novel framework that combines:  A Quantum Circuit Born Machine (QCBM) to enhance generative AI;  A Generative Adversarial Network (GAN) to design new molecules; A multi-agent reinforcement learning system to guide molecules toward key properties.

[New preprint 🎉] We’re excited to announce that our team has published new research on quantum computing for drug discovery! https://t.co/QC137EbcwL

【New preprint🎉】 We have posted a new paper on the quantum state simulation with a limited number of T gates, looking ahead the early-FTQC era.

[New preprint 🎉] We have posted a preprint proposing a way to realize quantum many-body scar states, which are intriguing nonthermal states, as a stable phase of nonequilibrium steady states. https://t.co/R5ESJ6b5RG

[Paper published 🎉] Our paper on estimating Clifford+T gate decomposition error has been published in Quantum. This work can lead to the efficient use of early fault-tolerant quantum computers. https://t.co/BkBeaBv9D9

We adopted a recently proposed powerful algorithm called generalized quantum signal processing (GQSP), and showed that it offers significant advantages over previous implementations in terms of computational cost and accuracy.

[New preprint🎉] We have posted a new paper on the realization of various power iteration methods for numerically solving eigenvalue problems on quantum computers, based on joint research led by UOsaka in collaboration with UTokyo.

Fluid dynamics simulation is a promising application for quantum computing, and we are actively working on it. We've just published a blog post on the work led by our intern Ueno, demonstrating the quantum circuit implementations for fluid dynamics. https://t.co/FksNKRXHxQ

【Objective insights for quantum computing adoption】 QunaSys has released the preview of “QURI Bench” — a benchmarking tool to evaluate quantum hardware for enterprise R&D and DX teams. Helping you choose the right hardware & accelerate development. https://t.co/LeMj8z7s0J

By applying this method to the excited states of aromatic molecules such as naphthalene and tetracene, we demonstrated on IBM quantum hardware that extending the configuration space can effectively compensate for quantum noise and improve computational accuracy.

《New preprint 🎉》 In collaboration with Toyota Central R&D Labs., Inc., we have published a paper on a computational method called "QSCI-PT," which applies multireference perturbation theory (MRPT) on quantum-selected electron configurations (QSCI). https://t.co/Fn2i4Erg46

Paper out! We prove worst-case classical hardness of sampling from unitary cluster Jastrow circuit, which automatically proves hardness for well-known UCC families. Congrats Hafid-san, Iwakiri-san, Kohda-san, and Kento for important step for QSCI/SQD! https://t.co/zlYdSDTCjS

This result serves as a foundational step toward establishing quantum advantage for sampling-based algorithms, such as quantum selected configuration interaction (QSCI).

《New preprint 🎉》 We’ve shown the classical hardness of simulating the sampling task from an ansatz state used in quantum chemistry and physics calculations on quantum computers, based on a widely accepted complexity-theoretic assumption. https://t.co/SbQBrg8tv2

While our previous study using VQE was limited to only 2 bands, our QSCI approach successfully enabled calculations involving 8 bands using 16 qubits. https://t.co/GJAR6XhqFe