NVIDIA Ansys and DCAI collaborate to advance quantum algorithms for fluid dynamics utilising the Gefion supercomputer and NVIDIA CUDAQ software GPUaccelerated simulations explore Quantum Lattice Boltzmann Methods assessing potential impacts on computational fluid dynamics and expanding hybrid quantumclassical computing capabilities

Diraq and QM Technologies integrate NVIDIA’s DGX Quantum architecture achieving 33microsecond communication between silicon quantum processors and GPUs This enables realtime readout enhancement automated calibration via machine learning and accelerated quantum state preparation The collaboration demonstrates rapid progress in hybrid quantumclassical computing and unlocks potential for future error correction

MerLin is a new quantum computing framework designed for data scientists not quantum physicists enabling rapid prototyping of hybrid quantumclassical machine learning models using familiar tools like PyTorch and scikitlearn it leverages GPU acceleration for simulating photonic quantum circuits and establishes reproducible benchmarks for algorithm evaluation aiming to bridge the gap between research and practical application

Caltech professor of chemistry Sandeep Sharma and colleagues from IBM and the RIKEN Center for Computational Science in Japan are giving us a glimpse of the future of computing. The team has used quantum computing in combination with classical distributed computing to attack a notably challenging problem in quantum chemistry: determining the electronic energy levels of a relatively complex molecule.

Postquant Labs announced the launch of a public quantum-classical blockchain testnet, attracting over 13,000 researchers. Built with D-Wave, it aims to advance quantum computing and a global blockchain standard.

NVIDIA Ansys and DCAI collaborate to advance quantum algorithms for fluid dynamics utilising the Gefion supercomputer and NVIDIA CUDAQ software GPUaccelerated simulations explore Quantum Lattice Boltzmann Methods assessing potential impacts on computational fluid dynamics and expanding hybrid quantumclassical computing capabilities

Diraq and QM Technologies integrate NVIDIA’s DGX Quantum architecture achieving 33microsecond communication between silicon quantum processors and GPUs This enables realtime readout enhancement automated calibration via machine learning and accelerated quantum state preparation The collaboration demonstrates rapid progress in hybrid quantumclassical computing and unlocks potential for future error correction

MerLin is a new quantum computing framework designed for data scientists not quantum physicists enabling rapid prototyping of hybrid quantumclassical machine learning models using familiar tools like PyTorch and scikitlearn it leverages GPU acceleration for simulating photonic quantum circuits and establishes reproducible benchmarks for algorithm evaluation aiming to bridge the gap between research and practical application