Designing Variational Quantum Algorithms with Tequila

Abstract

In the last few years, we have witnessed the proliferation of quantum computing languages. Many companies and research groups have developed their own tools to construct, simulate, and experimentally implement quantum algorithms. This fact has also accentuated the lack of standardized methods to solve problems using quantum computing. Heuristics play a crucial role in the development of new methods resulting in high demand for flexible and reliable ways to implement, test, and share new ideas. Inspired by all these demands, we introduce Tequila, a development package for quantum algorithms in python designed for fast and flexible implementation of novel quantum algorithms such as electronic structure and other fields. Tequila operates with abstract expectation values that can be combined, transformed, differentiated, and optimized in a blackboard style fashion. On evaluation, the abstract data structures are automatically compiled to run on state of the art quantum simulators or interfaces. In this talk, I will introduce the Tequila package, explain its current features, and present several usage examples.