GPU-Accelerated Host-Aware Dead-Measurement Detection in Hybrid Quantum--Classical Programs: Full Version

2026-07-10

AI Summary

This work addresses the challenge of semantically irrelevant measurements in hybrid quantum-classical programs, which hinder conventional optimizers from identifying host-semantic-aware redundant quantum gates. For the first time, host program semantics are integrated into dead measurement analysis through a novel static analysis framework based on abstract interpretation. The approach introduces a semantics-preserving, SSA-style intermediate representation specifically designed to enable GPU-parallel execution. Evaluated on 24 real-world hybrid workloads, the method removes an average of 37.98% of quantum gates; notably, it achieves over 30% additional gate reduction even after applying state-of-the-art optimizers such as Qiskit, t|ket⟩, and PyZX. The GPU-accelerated implementation demonstrates up to a 6.53× speedup.

Abstract

Hybrid programs combine a quantum circuit with a classical host program that consumes measurement outcomes. In such programs, an outcome may be syntactically read by the host but semantically non-contributory: changing the outcome cannot change the returned value. Such outcomes obscure gates that are dead only relative to the host semantics, and are therefore invisible to circuit-local optimizers. We present a semantics-aware host-side static analysis that identifies non-contributory measurement outcomes by abstract interpretation, and prove its soundness. We implement the analysis and evaluate it on $24$ application-faithful hybrid workloads across quantum chemistry, optimization, quantum machine learning, and quantum finance. Compared with a syntactic liveness baseline, our analysis identifies more than $4\times$ as many non-contributory measurements, and it standalone enables the removal of $37.98\%$ of total gates on average. Even after the state-of-the-art optimizers like Qiskit, t|ket$\rangle$, and PyZX have already optimized the circuits, our analysis still enables removal of more than $30\%$ of the post-optimized gates, showing that the host-semantic opportunities exposed by our analysis are not subsumed by circuit-local optimization. To scale our analysis, we further lower host programs to an SSA-style levelized intermediate representation that exposes level-wise parallelism for GPU execution, and implement a CUDA backend. We prove that this lowering preserves the analysis result, and the evaluation shows speedups of up to $6.53\times$ over a sequential baseline as structural parallelism increases.

Problem

• dead-measurement detection
• hybrid quantum-classical programs
• host-aware analysis
• semantic non-contributivity
• quantum circuit optimization

Innovation

• dead-measurement detection
• semantics-aware static analysis
• hybrid quantum-classical programs
• GPU acceleration
• abstract interpretation