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 blog, contains some hands-on examples using tequila. A good starting point is here.

Apart from that, you can find more information here:


Tequila is free an 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 me know in advance what you are planning.


The design of tequilas API was inspired by madness. Angostic backend handeling and forcing differetiability was inspired by pennylane.