AI summaryⓘ
The authors study hybrid quantum-classical programs where classical parts read measurement results from quantum circuits. Sometimes these measurement results don't actually affect the final output, even if the classical program looks at them. The authors created a new analysis that can find these 'non-contributory' measurement outcomes using program semantics, allowing much more effective removal of unnecessary quantum gates than existing methods. They show their method works well across various quantum applications and even improves after other optimizers have run. To make the analysis faster, they transformed the programs into a special intermediate form and ran the analysis on GPUs, achieving notable speedups.
hybrid quantum-classical programsquantum measurement outcomesstatic analysisabstract interpretationquantum circuit optimizationSSA (Static Single Assignment)CUDAperformance speedupquantum computinghost program semantics
Authors
Yanbin Chen, Qunyou Liu, Yu Wang, Christian B. Mendl, Helmut Seidl
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.