Explore @qBraid Open Source projects

@qBraid

qBraid

Stars
221
Global Org. Rank 38,308 (Top 13 %)
Registered almost 5 years ago
Most used languages

Jupyter Notebook
61.1 %

Python
27.8 %

JavaScript
5.6 %

HTML
5.6 %
Location 🇺🇸 United States
Country Total Rank 37,347
Country Ranking

Jupyter Notebook
1,524

Python
8,885

qBraid

A platform-agnostic quantum runtime framework

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.

Jupyter Notebook

HAQS-2022

Repository containing challenges for the qBraid HAQS 2022 quantum computing hackathon.

Jupyter Notebook

qbraid-qir

qBraid-SDK QIR transpiler integration

NYUAD-2022

Repository containing qBraid challenge for NYUAD Quantum Hackathon 2022

Jupyter Notebook

HAQS-QUSTEAM-2022

The HAQS QuSTEAM challenge

Jupyter Notebook

QCHack-2021

For submitting projects

Jupyter Notebook

QCHack-2022

Repository containing qBraid challenge for QCHack 2022

Jupyter Notebook

qbraid-algorithms

Build hybrid quantum-classical algorithms with qBraid

NYUAD-2023

qiskit-fall-fest-algiers

The qBraid platform instruction + challenge for Qiskit Fall Fest in Algeria

Jupyter Notebook

community

Where qBraid users discuss, report bugs and submit feature requests.

NCAT-QISE-Courses

Jupyter Notebook

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.

Jupyter Notebook

pyqasm

Python toolkit providing OpenQASM 3 semantic analyzer and utilities for program analysis and compilation.

IBM-Quantum-Challenge-2024

Jupyter Notebook

qBraid-Lab

Web-based JupyterLab deployment providing curated software tools for for researchers and developers in quantum computing.

QISE-NCAT

Jupyter Notebook

.github

Welcome to qBraid

presentations

micro-grants

Information for submitting quantum projects for qBraid micro grants.

jupyterhub-templates

Repo serving custom jinja templates to jupyterhub k8s deployments

q-site-braket