Enabling Dataflow Optimization for Quantum Programs

TL;DR

This paper introduces a new intermediate representation for quantum programs that exposes data dependencies, enabling static optimizations that improve resource efficiency with minimal compilation overhead.

Contribution

It presents QIRO, a novel IR with two dialects for quantum-classical co-optimization, facilitating dataflow analysis and static optimization of quantum programs.

Findings

Significant resource improvements through static optimization.

Low overhead in compilation time for optimized quantum programs.

Effective mapping from existing languages to the IR.

Abstract

We propose an IR for quantum computing that directly exposes quantum and classical data dependencies for the purpose of optimization. The Quantum Intermediate Representation for Optimization (QIRO) 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 run time, 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.