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 a 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.

变分量子算法是当前在近期量子计算机上部署的最有前途的算法类别。与经典算法相反，量子算法开发中几乎没有标准化的方法，并且该领域继续快速发展。与经典计算一样，启发式算法在新的量子算法的开发中起着至关重要的作用，因此对实现，测试和共享新思想的灵活而可靠的方法提出了很高的要求。受此需求的启发，我们介绍了龙舌兰酒，这是一种用于python中量子算法的开发包，旨在在电子结构和其他领域中快速灵活地实施，原型和部署新型量子算法。龙舌兰酒使用可以组合，转换，区分和优化的抽象期望值进行操作。