Welcome to qgrad’s documentation!¶
qgrad¶
qgrad is a python library that aims to make gradient-based optimization of quantum physics tasks easier for the users by bringing autodifferentiation to many commonly used quantum physics routines. qgrad reproduces essential QuTiP functions (with almost the same API) to reduce the friction for existing QuTiP users to transition to a new library. qgrad interfaces with the popular machine learning library, JAX, to make auto-differentiation of many quantum routines possible for desired learning tasks.
Disclaimer: qgrad is currently being developed in alpha mode, which may lead to changes in API. Track the latest developments on GitHub
Tutorials
API Reference
- API Reference
- Functions
- qgrad_qutip.basis
- qgrad_qutip.coherent
- qgrad_qutip.create
- qgrad_qutip.dag
- qgrad_qutip.destroy
- qgrad_qutip.expect
- qgrad_qutip.fidelity
- qgrad_qutip.isbra
- qgrad_qutip.isdm
- qgrad_qutip.isherm
- qgrad_qutip.isket
- qgrad_qutip.rand_unitary
- qgrad_qutip.rand_ket
- qgrad_qutip.rand_dm
- qgrad_qutip.sigmax
- qgrad_qutip.sigmay
- qgrad_qutip.sigmaz
- qgrad_qutip.squeeze
- qgrad_qutip.to_dm
- Classes
- Functions
Acknowledgements¶
qgrad was developed as part of Google Summer of Code (GSoC) 2020 project with NUMFOCUS and QuTiP. Asad Raza, the GSoC student from the City University of Hong Kong, was mentored by Shahnawaz Ahmed from Chalmers University of Technology and Nathan Shammah from the Unitary Fund to develop the library. We thank the organizations: GSoC, NUMFOCUS and QuTiP for funding the project and the developers for rolling out the first version of the package.
As part of GSoC, Asad has written several insructive blogs about the workings of the library, which can be found here. The package is still under development. Future roadmap of the package can be found in this wiki