用算法家族特征预测量子电路模拟的最低参数和耗时，省去反复试错。
Family-Aware Residual Architecture for Predicting Quantum Circuit Simulation Performance
- 基于算法家族差异设计可调残差修正模块，融合通用结构与特定模式。
- 91.2%在允许误差范围内预测阈值，运行时间相关性达R²=0.82。
- 适合量子算法开发者快速评估模拟可行性，提升设计效率。
近似张量网络模拟器使经典设备能模拟超出精确方法范围的量子电路，但选择最优近似参数（如键维数阈值）仍需耗费大量试错。本文提出一种家族感知神经架构，仅凭电路的OpenQASM描述和执行上下文，即可预测实现目标保真度所需的最小近似阈值及预期运行时间。核心洞察在于：不同算法家族（如QFT、Grover、VQE）因纠缠结构差异，其模拟成本特征根本不同。模型采用家族条件化残差修正——在共享主干上叠加家族特异性修正项，借鉴条件计算技术，捕捉通用性质与算法细节。架构整合了预训练家族分类器（准确率97.5%）及基于门组合启发式提取的领域导向算法指纹特征。在涵盖7–130量子比特、10种算法家族的电路上评估，系统达到79.5%阈值预测准确率（91.2%在允许误差范围内），运行时间相关性R²=0.82，单次推理约50毫秒；相较原方法显著减少需耗时数分钟至数小时的试错模拟。消融实验表明，家族感知建模带来最大性能提升（+3.2个百分点），验证算法家族是模拟成本预测的关键特征。
原文摘要 · Abstract (English)
Approximate tensor-network simulators enable classical simulation of quantum circuits beyond the reach of exact methods, but selecting optimal approximation parameters -- such as bond dimension thresholds -- remains a costly trial-and-error process. We present a family-aware neural architecture that predicts both the minimum approximation threshold required to achieve target fidelity and the expected wall-clock runtime for quantum circuit simulation, given only the circuit's OpenQASM description and execution context. Our key insight is that quantum circuits from different algorithmic families (e.g., QFT, Grover, VQE) exhibit fundamentally distinct simulation cost profiles due to their differing entanglement structures. We employ family-conditioned residual corrections -- additive, family-specific adjustments atop a shared backbone, drawing on established conditional computation techniques -- enabling the model to capture both universal circuit properties and algorithmic nuances. The architecture incorporates a pretrained family classifier (97.5% accuracy) and domain-informed algorithm fingerprint features derived from gate-composition heuristics. Evaluated on circuits spanning 7--130 qubits across 10 algorithm families, our system achieves 79.5% exact threshold accuracy (91.2% within one rung) and $R^2 = 0.82$ runtime correlation, with inference completing in approximately 50 ms -- replacing trial-and-error simulation runs that may take minutes to hours. Ablation studies confirm that family-aware modeling provides the single largest performance improvement (+3.2 percentage points), validating the hypothesis that algorithm family is a first-class feature for simulation cost prediction.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。