We propose an IR for quantum computing that directly exposes quantum and classical data dependencies for the purpose of optimization. Our IR consists of two dialects, one input dialect and one that is specifically tailored to enable quantum-classical co-optimization. While the first employs a perhaps more intuitive memory-semantics (quantum operations act as side-effects), the latter uses value-semantics (operations consume and produce states). Crucially, this encodes the dataflow directly in the IR, allowing for a host of optimizations that leverage dataflow analysis. We discuss how to map existing quantum programming languages to the input dialect and how to lower the resulting IR to the optimization dialect. We present a prototype implementation based on MLIR that includes several quantum-specific optimization passes. Our benchmarks show that significant improvements in resource requirements are possible even through static optimization. In contrast to circuit optimization at runtime, this is achieved while incurring only a small constant overhead in compilation time, making this a compelling approach for quantum program optimization at application scale.

中文翻译：

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为Quantum程序启用数据流优化

我们提出了一种用于量子计算的IR，它可以直接公开量子和经典数据的依赖关系，以达到优化的目的。我们的IR由两种方言组成，一种是输入方言，另一种是专门为实现量子经典协同优化而定制的。尽管前者采用的可能是更直观的记忆语义（量子运算充当副作用），而后者则采用了价值语义（运算消耗并产生状态）。至关重要的是，这直接在IR中对数据流进行了编码，从而允许利用数据流分析进行大量优化。我们讨论了如何将现有的量子编程语言映射到输入方言，以及如何将生成的IR降低到优化方言。我们提出了一种基于MLIR的原型实现，其中包括多个量子特定的优化过程。我们的基准测试表明，即使通过静态优化，也可以显着改善资源需求。与运行时的电路优化相反，这是在编译时仅产生很小的恒定开销的同时实现的，这使其成为在应用程序级进行量子程序优化的引人注目的方法。