Tequila is an abstraction framework for (variational) quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state of the art simulators as well as on real quantum devices.
This website contains some hands-on examples using tequila. A good starting point from a very fundamental tutorial is here.
The main sections of this website are:
- a wide collection of tutorials for getting to know tequila’s basic functionalities and general usage
- a collection of more specific tutorials regarding research
- a page with frequently asked questions regarding various aspects and usage of tequila FAQ
Contribute
Tequila is free and open source. You’re welcome to contribute if you have ideas to improve the library. The standard way to contribute is via pull-requests or issues on github. For larger projects it might be useful to let us know in advance what you are planning.
Influences
The design of tequilas API was inspired by madness. Agnostic backend handling and forcing differentiability was inspired by pennylane.