Efficient Transpilation of OpenQASM 3.0 Dynamic Circuits to CUDA-Q: Performance and Expressiveness Advantages

TL;DR

This paper introduces an open-source transpilation pipeline converting OpenQASM 3.0 dynamic circuits into optimized CUDA-Q kernels, enabling efficient execution of near-term quantum algorithms with classical control flow.

Contribution

It presents a novel method for translating OpenQASM 3.0 programs with classical control into CUDA-Q kernels, improving performance and expressiveness without static circuit expansion.

Findings

Reduces circuit depth by avoiding branch duplication

Improves execution efficiency through low-latency feedback

Enhances code readability by mapping control structures directly

Abstract

Dynamic quantum circuits with mid-circuit measurement and classical feedforward are essential for near-term algorithms such as error mitigation, adaptive phase estimation, and Variational Quantum Eigensolvers (VQE), yet transpiling these programs across frameworks remains challenging due to inconsistent support for control flow and measurement semantics. We present a transpilation pipeline that converts OpenQASM 3.0 programs with classical control structures (conditionals and bounded loops) into optimized CUDA-Q C++ kernels, leveraging CUDA-Q's native mid-circuit measurement and host-language control flow to translate dynamic patterns without static circuit expansion. Our open-source framework is validated on comprehensive test suites derived from IBM Quantum's classical feedforward guide, including conditional reset, if-else branching, multi-bit predicates, and sequential feedforward, and on VQE-style parameterized circuits with runtime parameter optimization. Experiments show that the resulting CUDA-Q kernels reduce circuit depth by avoiding branch duplication, improve execution efficiency via low-latency classical feedback, and enhance code readability by directly mapping OpenQASM 3.0 control structures to C++ control flow, thereby bridging OpenQASM 3.0's portable circuit specification with CUDA-Q's performance-oriented execution model for NISQ-era applications requiring dynamic circuit capabilities.