论文标题
龙舌兰酒：量子算法快速开发的平台
Tequila: A platform for rapid development of quantum algorithms
论文作者
论文摘要
各种量子算法是目前最有前途的算法类别用于近期量子计算机的算法。与经典算法相反，量子算法开发中几乎没有标准化方法，并且该领域继续迅速发展。与古典计算一样，启发式方法在新的量子算法的开发中起着至关重要的作用，从而对灵活和可靠的方式实施，测试和共享新想法的需求很高。受此需求的启发，我们引入了龙舌兰酒，这是Python中量子算法的开发包，旨在快速，灵活的实现，原型制作和电子结构和其他领域中新型量子算法的部署。龙舌兰酒具有抽象的期望值，可以组合，转换，差异化和优化。在评估时，汇编抽象数据结构以在最新的量子模拟器或接口上运行。
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.
