  Variational quantum algorithms are currently the most promising class of
algorithms for deployment on near-term quantum computers. In contrast to
classical algorithms, there are almost no standardized methods in quantum
algorithmic development yet, and the field continues to evolve rapidly. As in
classical computing, heuristics play a crucial role in the development of new
quantum algorithms, resulting in high demand for flexible and reliable ways to
implement, test, and share new ideas. Inspired by this demand, we introduce
tequila, a development package for quantum algorithms in python, designed for
fast and flexible implementation, prototyping, and deployment of novel quantum
algorithms in electronic structure and other fields. Tequila operates with
abstract expectation values which can be combined, transformed, differentiated,
and optimized. On evaluation, the abstract data structures are compiled to run
on state-of-the-art quantum simulators or interfaces.
