SymFT：基于符号Clifford–Pauli框架和稳定子坐标的通用容错量子电路模拟
SymFT: Universal Fault-Tolerant Quantum Circuit Simulation via Symbolic Clifford--Pauli Frames and Stabilizer Coordinates
AI总结：
SymFT是一款高吞吐量容错量子电路模拟器，通过符号Clifford–Pauli框架分解和自适应稳定子坐标规划优化采样，在多种量子电路上实现了比Stim、Clifft、SOFT更优的采样性能。
AI中文摘要：
容错协议通常主要由稳定子子电路构成，但实现通用性所需的非Clifford操作使精确采样成本高昂。我们提出SymFT，一种针对以Clifford为主、包含Pauli旋转、随机Pauli噪声、 mid-circuit Pauli测量以及测量记录控制的Pauli反馈的电路的高吞吐量模拟器。它结合了两种思路：其一，符号Clifford–Pauli框架分解将分支概率采样简化为Pauli旋转和测量投影，噪声与反馈以符号符号表示；由于剩余Clifford和Pauli框架是幺正的，它们不影响分支概率，无需在每次采样中应用。其二，自适应稳定子坐标规划使用共享的稳定子–去稳定子表格定义基，仅将活动非稳定子自由度存储在动态大小的密集活动态向量中。该方法仅解析一次基变化，直接输出多坐标采样指令，从而避免每次采样的表格更新和密集向量因局域化导致的Clifford变换。在测试的纯Clifford和近Clifford电路中，SymFT实现了最先进的采样性能：在单个CPU核心上，针对表面码电路，它比Stim快2.51–2.56倍；针对魔法态制备和蒸馏电路，比Clifft快1.86–3.51倍；针对测试的制备电路，其采样吞吐量比我们之前的模拟器SOFT高出两个数量级以上。
英文摘要：
Fault-tolerant protocols often consist largely of stabilizer subcircuits, yet the non-Clifford operations required for universality make exact sampling costly. We present SymFT, a high-throughput simulator for Clifford-dominated circuits with Pauli rotations, stochastic Pauli noise, mid-circuit Pauli measurements, and measurement-record-controlled Pauli feedback. It combines two ideas. First, symbolic Clifford--Pauli frame factorization reduces branch-probability sampling to Pauli rotations and measurement projectors, with noise and feedback represented by symbolic signs. Since the residual Clifford and Pauli frames are unitary, they do not affect branch probabilities and need not be applied in every shot. Second, adaptive stabilizer-coordinate planning uses a shared stabilizer--destabilizer tableau to define the basis and stores only the active non-stabilizer degrees of freedom in a dynamically sized dense active-state vector. It resolves basis changes once and emits direct multi-coordinate sampling instructions, thereby avoiding per-shot tableau updates and localization-induced Clifford transformations of the dense vector. Across the tested pure-Clifford and near-Clifford circuits, SymFT achieves state-of-the-art sampling performance. On a single CPU core, it achieves a $2.51\text{--}2.56\times$ speedup over Stim for surface-code circuits and a $1.86\text{--}3.51\times$ speedup over Clifft for magic-state cultivation and distillation circuits. For the tested cultivation circuits, its GPU sampling throughput also exceeds that of our previous simulator, SOFT, by more than two orders of magnitude.
