AI + Simulation

How hybrid AI and quantum simulation systems are accelerating materials, drug discovery, and finance by combining neural models with quantum processors.

RIKEN and Q-CTRL used AI-guided circuit design and Fire Opal error suppression to simulate open quantum systems for 40 time steps on IBM and Quantinuum hardware.

How the SCALAR framework uses AI, CUDA-Q, txGraffiti and LLMs to find patterns in QAOA circuits and what that means for quantum algorithm design.

Taiyi Quantum closes 300M yuan Pre-A. Using ytterbium neutral atoms, the company targets cryptography, chemical simulation and quantum machine learning.

How next-generation storage and data-path offloads solve KV cache bottlenecks, speed LLM inference, and enable large-scale quantum simulation workloads.

How exascale AI factories, hybrid HPC-AI-quantum workflows and Germany’s GCS are shaping research and applications ahead of ISC 2026.

How collaborative AI platforms accelerate AI simulation and quantum research using neural networks, data-driven insights, and expert ecosystems.

How AI agents, MLIPs and large standardized datasets like Open Catalyst speed catalyst discovery, bridge lab-to-industry scale-up, and point toward autonomous labs.

How AI-driven quantum simulation is changing drug discovery, materials design, and modeling. Clear explanation of the breakthrough, uses, and near-term outlook.

Nissan and Quemix used hybrid quantum-classical algorithms to run vehicle aerodynamic simulations in minutes instead of days, cutting development time and opening new AI-driven design possibilities.