replaced A.Dagger() by Dagger().permute_(A.labels())

this achieves that the output of the Dagger is in the same form as the original tensor

Author: manuschneider

example/DMRG/dmrg_two_sites_U1.py

@@ -111,10 +111,11 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
     anet.FromString(["L: -2,-1,-3",\
                     "A: -1,-4,1",\
                     "M: -2,0,-4,-5",\
-                    "A_Conj: 2,-3,-5",\
+                    "A_Conj: -3,-5,2",\
                     "TOUT: 0;1,2"])
     for p in range(Nsites - 1):
-        anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Dagger(),M])
+        anet.PutUniTensors(["L","A","A_Conj","M"], \
+                           [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
         LR[p+1] = anet.Launch()
 
     Ekeep = []
@@ -143,9 +144,10 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
             anet.FromString(["R: -2,-1,-3",\
                         "B: 1,-4,-1",\
                         "M: 0,-2,-4,-5",\
-                        "B_Conj: -3,2,-5",\
+                        "B_Conj: 2,-3,-5",\
                         "TOUT: 0;1,2"])
-            anet.PutUniTensors(["R","B","M","B_Conj"],[LR[p+2],A[p+1],M,A[p+1].Dagger()])
+            anet.PutUniTensors(["R","B","M","B_Conj"], \
+                               [LR[p+2],A[p+1],M,A[p+1].Dagger().permute_(A[p+1].labels())])
             LR[p+1] = anet.Launch()
 
             print('Sweep[r->l]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))
@@ -177,9 +179,10 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
             anet.FromString(["L: -2,-1,-3",\
                         "A: -1,-4,1",\
                         "M: -2,0,-4,-5",\
-                        "A_Conj: 2,-3,-5",\
+                        "A_Conj: -3,-5,2",\
                         "TOUT: 0;1,2"])
-            anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Dagger(),M])
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
             LR[p+1] = anet.Launch()
 
             print('Sweep[l->r]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))

example/DMRG/dmrg_two_sites_dense.py

@@ -92,7 +92,8 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
                         "A_Conj: -3,-5,2",\
                         "TOUT: 0,1,2"])
         # or you can do: anet = cytnx.Network("L_AMAH.net")
-        anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+        anet.PutUniTensors(["L","A","A_Conj","M"], \
+                           [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
         LR[p+1] = anet.Launch()
 
         # Recover the original MPS labels
@@ -131,7 +132,8 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
                             "B_Conj: 2,-5,-3",\
                             "TOUT: 0;1,2"])
             # or you can do: anet = cytnx.Network("R_AMAH.net")
-            anet.PutUniTensors(["R","B","M","B_Conj"],[LR[p+2],A[p+1],M,A[p+1].Conj()])
+            anet.PutUniTensors(["R","B","M","B_Conj"], \
+                               [LR[p+2],A[p+1],M,A[p+1].Dagger().permute_(A[p+1].labels())])
             LR[p+1] = anet.Launch()
 
             print('Sweep[r->l]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))
@@ -167,7 +169,8 @@ def optimize_psi(psi, functArgs, maxit=2, krydim=4):
                             "TOUT: 0,1,2"])
             # or you can do: anet = cytnx.Network("L_AMAH.net")
 
-            anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
             LR[p+1] = anet.Launch()
 
             print('Sweep[l->r]: %d/%d, Loc: %d,Energy: %f' % (k, numsweeps, p, Ekeep[-1]))

example/TDVP/tdvp1_dense.py

@@ -80,7 +80,8 @@ def get_energy(A, M):
                         "TOUT: 0,1,2"])
         # or you can do: anet = cytnx.Network("L_AMAH.net")
         for p in range(0, N):
-            anet.PutUniTensors(["L","A","A_Conj","M"],[L,A[p],A[p].Conj(),M])
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [L,A[p],A[p].Dagger().permute_(A[p].labels()),M])
             L = anet.Launch()
         E = cytnx.Contract(L, R0).item()
         print('energy:', E)
@@ -138,7 +139,8 @@ def get_energy(A, M):
                         "A_Conj: -3,-5,2",\
                         "TOUT: 0,1,2"])
         # or you can do: anet = cytnx.Network("L_AMAH.net")
-        anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+        anet.PutUniTensors(["L","A","A_Conj","M"], \
+                           [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
         LR[p+1] = anet.Launch()
 
         # Recover the original MPS labels
@@ -175,7 +177,8 @@ def get_energy(A, M):
                             "B_Conj: 2,-5,-3",\
                             "TOUT: ;0,1,2"])
             # or you can do: anet = cytnx.Network("R_AMAH.net")
-            anet.PutUniTensors(["R","B","M","B_Conj"],[LR[p+1],A[p],M,A[p].Conj()])
+            anet.PutUniTensors(["R","B","M","B_Conj"], \
+                               [LR[p+1],A[p],M,A[p].Dagger().permute_(A[p].labels())])
             old_LR = LR[p].clone()
             if p != 0:
                 LR[p] = anet.Launch()
@@ -215,7 +218,8 @@ def get_energy(A, M):
                             "A_Conj: -3,-5,2",\
                             "TOUT: 0,1,2"])
 
-            anet.PutUniTensors(["L","A","A_Conj","M"],[LR[p],A[p],A[p].Conj(),M])
+            anet.PutUniTensors(["L","A","A_Conj","M"], \
+                               [LR[p],A[p],A[p].Dagger().permute_(A[p].labels()),M])
             old_LR = LR[p+1].clone()
 
 
@@ -246,15 +250,17 @@ def Local_meas(A, B, Op, site):
                     "TOUT: 2;4"])
     for i in range(0, N):
         if i != site:
-            anet.PutUniTensors(["l","A","B"],[l,A[i],B[i].Conj()])
+            anet.PutUniTensors(["l","A","B"], \
+                               [l,A[i],B[i].Dagger().permute_(B[i].labels())])
             l = anet.Launch()
         else:
             tmp = A[i].relabel(1, "_aux_up")
             Op = Op.relabels(["_aux_up", "_aux_low"])
             tmp = cytnx.Contract(tmp, Op)
             tmp.relabel_("_aux_low", A[i].labels()[1])
             tmp.permute_(A[i].labels())
-            anet.PutUniTensors(["l","A","B"],[l,tmp,B[i].Conj()])
+            anet.PutUniTensors(["l","A","B"], \
+                               [l,tmp,B[i].Dagger().permute_(B[i].labels())])
             l = anet.Launch()
 
     return l.reshape(1).item()