diff --git a/tests/test_tensor/test_optimizers.py b/tests/test_tensor/test_optimizers.py
index 4a3b3c9b..0b67b8e7 100644
--- a/tests/test_tensor/test_optimizers.py
+++ b/tests/test_tensor/test_optimizers.py
@@ -78,12 +78,12 @@ def heis_pbc():
     H = qtn.MPO_ham_heis(L, cyclic=True).astype(dtype)
 
     def norm_fn(psi):
-        factor = (psi & psi).contract(all, optimize='random-greedy')
+        factor = (psi & psi).contract(all, optimize="auto-hq")
         return psi / factor**0.5
 
     def loss_fn(psi, H):
         k, H, b = qtn.tensor_network_align(psi, H, psi)
-        energy = (k & H & b).contract(all, optimize='random-greedy')
+        energy = (k & H & b).contract(all, optimize="auto-hq")
         return energy
 
     en_ex = qu.groundenergy(qu.ham_heis(L, cyclic=True, sparse=True))
@@ -102,12 +102,12 @@ def ham_mbl_pbc_complex():
     H = qtn.MPO_ham_mbl(L, **ham_opts).astype(dtype)
 
     def norm_fn(psi):
-        factor = (psi.H & psi).contract(all, optimize='random-greedy')
+        factor = (psi.H & psi).contract(all, optimize="auto-hq")
         return psi * factor**-0.5
 
     def loss_fn(psi, H):
         k, H, b = qtn.tensor_network_align(psi, H, psi.H)
-        energy = (k & H & b).contract(all, optimize='random-greedy')
+        energy = (k & H & b).contract(all, optimize="auto-hq")
         return real(energy)
 
     en_ex = qu.groundenergy(qu.ham_mbl(L, sparse=True, **ham_opts))
diff --git a/tests/test_tensor/test_tensor_2d.py b/tests/test_tensor/test_tensor_2d.py
index 16dafb1f..710498f6 100644
--- a/tests/test_tensor/test_tensor_2d.py
+++ b/tests/test_tensor/test_tensor_2d.py
@@ -40,7 +40,7 @@ def test_basic_rand(self, Lx, Ly):
                 assert isinstance(psi[f"I{i},{j}"], qtn.Tensor)
 
         if Lx == Ly == 3:
-            psi_dense = psi.to_qarray(optimize="random-greedy")
+            psi_dense = psi.to_qarray(optimize="auto-hq")
             assert psi_dense.shape == (512, 1)
 
         psi.show()
@@ -392,7 +392,7 @@ def test_compute_local_expectation_one_sites(self, mode, normalized):
         )
         ex = qu.expec(A, k)
 
-        opts = dict(cutoff=2e-3, max_bond=9, contract_optimize="random-greedy")
+        opts = dict(cutoff=2e-3, max_bond=9, contract_optimize="auto-hq")
         e = peps.compute_local_expectation(
             terms, mode=mode, normalized=normalized, **opts
         )
@@ -417,7 +417,7 @@ def test_compute_local_expectation_two_sites(self, mode, normalized):
             normalized=normalized,
             cutoff=2e-3,
             max_bond=16,
-            contract_optimize="random-greedy",
+            contract_optimize="auto-hq",
         )
 
         # compute 2x1 and 1x2 plaquettes separately
@@ -488,7 +488,7 @@ def test_basic_rand(self, Lx, Ly):
                 assert isinstance(X[f"I{i},{j}"], qtn.Tensor)
 
         if Lx == Ly == 3:
-            X_dense = X.to_qarray(optimize="random-greedy")
+            X_dense = X.to_qarray(optimize="auto-hq")
             assert X_dense.shape == (512, 512)
             assert qu.isherm(X_dense)