Predicting practical speedups offered by future quantum computers has become a major focus of the quantum community. Typically, such predictions involve numerical simulations supported by lengthy manual analyses and are carried out for one specific algorithm at a time. In this work, we present Traq, a principled approach towards estimating the quantum speedup of classical programs fully automatically. It consists of a classical language that includes high-level primitives amenable to quantum speedups, a compilation to low-level quantum programs, and a source-level cost analysis with provable guarantees. Our cost analysis upper bounds the complexity of the resulting quantum program and is sensitive to the input data of the program (in addition to providing worst-case costs). Traq is implemented as a Haskell package with an extensive evaluation.

翻译：预测未来量子计算机所带来的实际加速已成为量子社区的主要关注点。通常，此类预测涉及由冗长手动分析支持的数值模拟，并且一次仅针对特定算法进行。在本文中，我们提出 Traq，一种全自动估算经典程序量子加速的原则性方法。它包含一个具有高级原语的经典语言（这些原语可受益于量子加速），一个到低级量子程序的编译器，以及一个具有可证明保证的源级代价分析。我们的代价分析对所得量子程序的复杂度给出了上界，并且对程序的输入数据敏感（除提供最坏情况代价外）。Traq 已实现为一个带有广泛评估的 Haskell 包。