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

2026-04-13

AI Summary

This work addresses the challenges posed by semantic mismatches in control flow and measurement handling when translating dynamic quantum circuits across frameworks. It proposes an efficient translation pipeline that, for the first time, enables direct mapping of OpenQASM 3.0 programs—with conditional branching and bounded loops—into CUDA-Q C++ kernels, without resorting to static unrolling or branch duplication. By leveraging CUDA-Q’s native support for mid-circuit measurements and host-language control flow, the approach significantly reduces circuit depth, enhances execution efficiency, and improves code readability. The method’s effectiveness is validated on NISQ-era algorithms such as VQE, demonstrating a favorable balance between portability and performance.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Computer Vision: 3D Computer Vision

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

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.

Problem

• dynamic quantum circuits
• transpilation
• OpenQASM 3.0
• classical feedforward
• mid-circuit measurement

Innovation

• dynamic quantum circuits
• OpenQASM 3.0
• CUDA-Q
• classical feedforward
• transpilation