qBraid's repositories
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.
NYUAD-2022
Repository containing qBraid challenge for NYUAD Quantum Hackathon 2022
qbraid-qir
qBraid-SDK QIR transpiler integration
HAQS-QUSTEAM-2022
The HAQS QuSTEAM challenge
QCHack-2021
For submitting projects
QCHack-2022
Repository containing qBraid challenge for QCHack 2022
qiskit-fall-fest-algiers
The qBraid platform instruction + challenge for Qiskit Fall Fest in Algeria
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-algorithms
Build hybrid quantum-classical algorithms with qBraid
qBraid-Lab
Web-based JupyterLab deployment providing curated software tools for for researchers and developers in quantum computing.
jupyter-resource-usage
Jupyter Notebook Extension for monitoring your own Resource Usage
QuEra-braket-examples
Using QuEra's Aquila quantum computer on qBraid.
amazon-braket-examples
Example notebooks that show how to apply quantum computing with Amazon Braket.
botocore
The low-level, core functionality of boto3 and the AWS CLI.
Cirq
A python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits.
gpu-jupyter
Leverage the flexibility of Jupyterlab through the power of your NVIDIA GPU to run your code from Tensorflow and Pytorch in collaborative notebooks on the GPU.
IEEE_QCE23_qBraid_Tutorial
Tutorial prepared for the IEEE QCE23 conference.
jupyter-foundation-docker-stacks
Ready-to-run Docker images containing Jupyter applications
OpenFermion
The electronic structure package for quantum computers.
python-training
Python training for business analysts and traders
qiskit-aer
Aer is a high performance simulator for quantum circuits that includes noise models
qiskit-algorithms
A library of quantum algorithms for Qiskit.