添加MPI并行TN benchmark及辅助脚本，移除旧benchmark

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

compare_jit_tn_quimb.py

@@ -0,0 +1,51 @@
+import numpy as np
+import os
+import sys
+
+def check_results(ref_path, tn_path):
+    # 1. 检查文件是否存在
+    if not os.path.exists(ref_path) or not os.path.exists(tn_path):
+        print(f"Error: 找不到文件！\n参考文件: {ref_path}\n待测文件: {tn_path}")
+        return
+
+    print(f"正在加载数据并对比: \n  [Ref] {ref_path}\n  [TN ] {tn_path}\n")
+
+    try:
+        # 2. 加载状态向量
+        # mmap_mode='r' 可以防止大文件直接撑爆内存
+        sv_ref = np.load(ref_path, mmap_mode='r')
+        sv_tn = np.load(tn_path, mmap_mode='r')
+
+        # 3. 计算保真度 (Fidelity)
+        # fid = |<ref|tn>|^2
+        inner_product = np.dot(sv_ref.conj(), sv_tn)
+        fidelity = np.abs(inner_product)**2
+
+        # 4. 计算 L2 误差 (欧氏距离)
+        l2_error = np.linalg.norm(sv_ref - sv_tn)
+
+        # 5. 打印结果
+        print("-" * 30)
+        print(f"保真度 (Fidelity): {fidelity:.12f}")
+        #print(f"L2 范数误差:       {l2_error:.2e}")
+        print("-" * 30)
+
+        # phase-invariant L2: align global phase first
+        phase = inner_product / np.abs(inner_product)
+        l2_phase_corrected = np.linalg.norm(sv_ref - sv_tn / phase)
+        print(f"L2 误差(相位校正后): {l2_phase_corrected:.2e}")
+
+        if fidelity > 0.999999:
+            print("✅ 验证通过：结果高度一致。")
+        else:
+            print("❌ 警告：保真度较低，请检查收缩路径或截断误差。")
+
+    except Exception as e:
+        print(f"计算过程中发生错误: {e}")
+
+if __name__ == "__main__":
+    # 你可以在这里直接修改文件名
+    REF_FILE = 'data/sv_qibojit_qft30.npy'
+    TN_FILE  = 'data/sv_tn_qft30_mpi.npy'
+    
+    check_results(REF_FILE, TN_FILE)