Tequila：量子算法快速开发平台
Tequila: A platform for rapid development of quantum algorithms
摘要：
变分量子算法是目前最有希望在近期量子计算机上部署的一类算法。与经典算法相比，量子算法的发展几乎没有标准化的方法，而且该领域仍在快速发展。与经典计算一样，量子力学在新量子算法的开发中发挥着至关重要的作用，因此对灵活可靠的方法来实现，测试和共享新想法的需求很高。受此需求的启发，我们引入了tequila，一个python中的量子算法开发包，旨在快速灵活地实现，原型设计和部署电子结构和其他领域的新型量子算法。Tequila使用抽象的期望值进行操作，这些期望值可以被组合，转换，区分和优化。在评估时，抽象数据结构被编译为在最先进的量子模拟器或接口上运行。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
