Update lindbladian

src/models/contact_process_model.py

@@ -121,7 +121,7 @@ def construct_num_op(L: int) -> tt.TT:
 
     for i in range(L):
         number_op = np.kron(np.array([[0, 0], [0, 1]]), np.eye(2)) + np.kron(np.eye(2), np.array([[0, 0], [0, 1]]))
-        op_cores[i] = np.zeros([2, 2, 2, 2], dtype=complex)
+        # op_cores[i] = np.zeros([2, 2, 2, 2], dtype=complex)
         op_cores[i] = number_op.reshape(2, 2, 2, 2)
 
     return tt.TT(op_cores)

src/simulations/contact_process_stat.py

@@ -68,6 +68,7 @@
         # compute Hermitian part of mps
         hermit_mps = (1 / 2) * (gs_mps + gs_mps_dag)
 
+        # compute observables
         print("Compute particle numbers")
         particle_nums = compute_site_expVal_vMPO(hermit_mps, construct_num_op(L))
         print(f"Particle number/site: {particle_nums}")
@@ -83,10 +84,11 @@
             purities[i] = compute_purity(gs_mps)
             print(f"Purity: {purities[-1]}")
 
-            print("Compute half-chain density correlation for largest bond dimension")
+            print("Compute density correlation for largest bond dimension")
             an_op = construct_num_op(1)
             for k in range(L - 1):
-                correlations[i, k] = abs(compute_correlation_vMPO(gs_mps, an_op, r0=0, r1=k + 1))
+                correlations[i, k] = abs(compute_correlation_vMPO(gs_mps, an_op, r0=0,
+                                                                  r1=k + 1))  #NOTE: absolute value or not??
 
             print("Compute half-chain entanglement entropy spectrum for largest bond dimension")
             ent_ent_spectrum[i, :] = compute_entanglement_spectrum(gs_mps)

src/utilities/utils.py

@@ -73,6 +73,8 @@ def compute_purity(mps: tt.TT) -> float:
 
 def compute_site_expVal(mps: tt.TT, mpo: tt.TT) -> np.ndarray:
     """
+    Compute the expectation value < Ψ | onsiteOp | Ψ > for each site of the MPS 
+
     Args:
     - mps: canonicalized mps
     - mpo: cores that have dimension (2,2,2,2)
@@ -96,7 +98,7 @@ def compute_site_expVal(mps: tt.TT, mpo: tt.TT) -> np.ndarray:
 
 def compute_site_expVal_vMPO(mps: tt.TT, mpo: tt.TT) -> np.ndarray:
     """
-    Compute ā = (1/L) *  Σ_k Tr(ρ A_k)
+    Compute Tr(ρ A_k) for each k
     """
 
     site_vals = np.zeros(mps.order, dtype=float)

tests/test_model.py

@@ -8,23 +8,23 @@
 import numpy as np
 
 import src.models.contact_process_model as cp_model
-import src.models.ising_model as ising_model
+import src.models.diss_ising_model as diss_ising_model
 
 logger = logging.getLogger(__name__)
 logging.basicConfig(level=logging.INFO)
 
 L = 3
-GAMMA = 1
+GAMMA = 1.0
 OMEGA = 1.5
-V = 5
-DELTA = 0
+V = 5.0
+DELTA = 0.0
 
 
 class ModelFunctions(unittest.TestCase):
 
     def test_hermiticity(self):
-        ising_lindblad = ising_model.construct_lindblad(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
-        ising_lindblad_dag = ising_model.construct_lindblad_dag(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
+        ising_lindblad = diss_ising_model.construct_lindblad(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
+        ising_lindblad_dag = diss_ising_model.construct_lindblad_dag(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
         assert np.array_equal(ising_lindblad.transpose(conjugate=True).matricize(), ising_lindblad_dag.matricize())
 
         ising_lindblad_hermitian = ising_lindblad_dag @ ising_lindblad
@@ -37,16 +37,16 @@ def test_hermiticity(self):
         cp_lindblad_hermitian = cp_lindblad_dag @ cp_lindblad
         assert np.array_equal(np.conj(cp_lindblad_hermitian.matricize().T), cp_lindblad_hermitian.matricize())
 
-    def test_exact_diagonalization(self):  #TODO: Add ising model too :)
+    def test_exact_diagonalization(self):
         cp_lindblad = cp_model.construct_lindblad(gamma=GAMMA, omega=OMEGA, L=L)
         cp_lindblad_dag = cp_model.construct_lindblad_dag(gamma=GAMMA, omega=OMEGA, L=L)
         cp_lindblad_hermitian = cp_lindblad_dag @ cp_lindblad
 
         evals, _ = np.linalg.eig(cp_lindblad_hermitian.matricize())
         assert np.isclose(np.min(evals.real), 0.0)
 
-        ising_lindblad = ising_model.construct_lindblad(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
-        ising_lindblad_dag = ising_model.construct_lindblad_dag(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
+        ising_lindblad = diss_ising_model.construct_lindblad(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
+        ising_lindblad_dag = diss_ising_model.construct_lindblad_dag(gamma=GAMMA, V=V, omega=OMEGA, delta=DELTA, L=L)
         ising_lindblad_hermitian = ising_lindblad_dag @ ising_lindblad
 
         evals, _ = np.linalg.eig(ising_lindblad_hermitian.matricize())