Pinned Repositories
community
Where qBraid users discuss, report bugs and submit feature requests.
HAQS-2022
Repository containing challenges for the qBraid HAQS 2022 quantum computing hackathon.
HAQS-QUSTEAM-2022
The HAQS QuSTEAM challenge
NYUAD-2022
Repository containing qBraid challenge for NYUAD Quantum Hackathon 2022
qBraid
A platform-agnostic quantum runtime framework
qBraid-Lab
Web-based JupyterLab deployment providing curated software tools for for researchers and developers in quantum computing.
qbraid-lab-demo
Notebooks demonstrating how to use qBraid Lab to streamline quantum workflows, connect to quantum hardware, and leverage GPU-enabled scalable compute for hybrid algorithms.
qbraid-qir
qBraid-SDK QIR transpiler integration
QCHack-2021
For submitting projects
QCHack-2022
Repository containing qBraid challenge for QCHack 2022
qBraid's Repositories
qBraid/QCHack-2021
For submitting projects
qBraid/BMW_Challenge
Due to the rapid development of hardware and software, the past decades have drastically shifted quality control from manual examination towards automated inspection. In the light of the required human expertise to hand-tune algorithms, machine learning (ML) techniques promise a more general and scalable approach to quality control. The remarkable success of convolutional neural networks (CNNs) in image processing has revolutionized automated quality inspection. Of course, any technology has its limitation, and for CNNs, it is computation power. As high-performance CNNs usually assume large datasets, datacenters ultimately end up with large numerical workloads and expensive GPUs. Quantum computing may one day break through classical computational bottlenecks, providing faster and more efficient training with higher accuracy.
qBraid/micro-grants
Information for submitting quantum projects for qBraid micro grants.
qBraid/presentations
presentations