这篇论文用晶圆缺陷分类任务硬核对比了两种量子计算范式，CV在4个qumodes下比DV高出18个点，还解释了为什么。搞量子机器学习或半导体良率的朋友可以看看。
该论文在WM-811K晶圆图缺陷分类任务（8类）上，控制卷积主干（约430万参数）不变，仅替换头部为经典全连接、连续变量（CV）或离散变量（DV）量子神经网络（QNN），并缩放至3、4、8个qumodes/qubits。在4个qumodes/qubits时，CV-QNN达到79.7±1.8%准确率，DV-QNN仅61.6±1.4%，差距18个百分点。CV对空间局部的Edge-Loc类召回率达0.66±0.06，而DV始终低于0.05。实验表明CV优势来自结构化层和连续相空间编码，而非希尔伯特空间维度；经典基线为85.0%，但控制实验揭示了结构头部的潜力。
Bridging Quantum Computing Paradigms toward Semiconductor Yield: A Controlled CV-versus-DV Comparison on Wafer-Map Defect Classification
Realizing quantum neural networks (QNNs) in industry requires knowing which quantum computing paradigm suits which task. Motivated by AI accelerators and high-bandwidth memory, where die stacking makes wafer-level defect screening central to yield, we study WM-811K wafer-map defect classification (eight classes), comparing the dominant paradigms, continuous-variable (CV) and discrete-variable (DV), under controlled conditions. To isolate the quantum circuit as the sole variable, a shared convolutional backbone (~4.3M parameters) feeds interchangeable heads (classical dense, CV-QNN, or DV-QNN) as the only structural difference; each quantum head is scaled over three sizes (3, 4, 8 qumodes/qubits). The CV head consistently outperforms the DV head: at four qumodes/qubits it reaches 79.7 +/- 1.8% accuracy versus 61.6 +/- 1.4%, a non-overlapping 18-point gap. The advantage is sharpest on the spatially localized Edge-Loc class, easily confused with Scratch, which CV recovers with recall 0.66 +/- 0.06 while DV fails at every size (<=0.05), showing the structured CV layer better captures fine spatial distinctions between defect types. Training curves show the DV limitation is a representational-capacity ceiling, not an optimization failure; at the Fock cutoff used here (d = 2) the CV advantage reflects two intrinsic properties, a structured, neural-network-analogue layer and continuous phase-space encoding, not Hilbert-space dimensionality. On IBM hardware, DV accuracy holds at shallow depth, degrading only at the deepest circuit. Both quantum heads remain below the classical baseline (85.0%), but the controlled setting isolates where a structured head already helps and, as noise and scale improve, which paradigm can deliver practical advantage.