Tequila: A platform for rapid development of quantum algorithms

TL;DR

Tequila is a Python-based platform that enables rapid, flexible development, testing, and deployment of variational quantum algorithms, facilitating innovation in quantum computing research and applications.

Contribution

It introduces a novel software package that simplifies the implementation and testing of quantum algorithms with abstract expectation values and compatibility with quantum simulators.

Findings

Supports fast prototyping of quantum algorithms

Allows flexible combination and transformation of expectation values

Compatible with state-of-the-art quantum simulators

Abstract

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.