qBraid is continuously updating and improving its features! Here's an FAQ page for those who are interested in learning more. All resources are also available on our docs.
qBraid is a platform that provides a seamless environment for coding and accessing quantum computers for research.
Quantum Computers enable a paradigm shift. From bits where information is stored only in 0 or 1, we leap forwards with qubits to a superposition of 0 and 1.
A platform to support solutions using different frameworks and real quantum computers to create applications in multiple fields, such as chemistry, and optimization, among others areas.
There is access to the qBraid Lab and different tutorials that one can access to get started in quantum computing. Learn more abou qBraid Lab →
qBraid Lab users can get direct access to QPU devices from IonQ, Oxford Quantum Circuits, QuEra, Rigetti, and IQM. Learn more about Quantum Jobs →
There are different tutorials in the platform in which you have to work with the default qbraid kernel to validate that the SDK is supported in the virtual lab and even connect your code to a QPU (check more qbraid-lab-demo). To install more dependencies, like AWS, Qiskit, etc., you must have credits in your account. Learn more about getting started →
Access to both classical and quantum computing resources is managed through the qBraid credits system, which operates on a pay-as-you-go basis. Each qBraid credit is worth $0.01 USD, so a quantum job costing $3.80 would subtract 380 credits from your qBraid balance. Credits can be purchased from your account page, or redeemed using an access key. You can check your current credit balance on your account page, in the qBraid Lab JOBS
sidebar, or using the qBraid-CLI. Learn more about pricing →
qBraid currently supports 3 open-source projects in quantum computing, each of which are always welcoming new contributors!
- qBraid-SDK: A platform-agnostic quantum runtime framework
- qBraid-QIR: qBraid-SDK extension providing support for QIR conversions.
- qBraid-Algorithms: Python package for building, simulating, and benchmarking hybrid quantum-classical algorithms