Minor fix for scipy.sparse representation format

Author: SimoneGasperini

src/qunfold/simulator.py

@@ -9,10 +9,11 @@
 
 
 class IsingHamiltonianSimulator:
-    def __init__(self, anneal_schedule=None, time_steps=100, dtype=np.float64):
+    def __init__(self, anneal_schedule=None, time_steps=100, sparse_format="csr", sparse_dtype=np.float64):
         self.anneal_schedule = self.default_anneal_schedule if anneal_schedule is None else anneal_schedule
         self.time_steps = time_steps
-        self.dtype = dtype
+        self.sparse_format = sparse_format
+        self.sparse_dtype = sparse_dtype
 
     @staticmethod
     def default_anneal_schedule(s):
@@ -22,18 +23,18 @@ def default_anneal_schedule(s):
 
     def get_H_init(self, num_qubits):
         size = 2**num_qubits
-        H_init = sparse.csr_array((size, size), dtype=self.dtype)
-        i_sparse = sparse.csc_array(identity, dtype=self.dtype)
-        x_sparse = sparse.csc_array(sigma_x, dtype=self.dtype)
+        H_init = sparse.csr_array((size, size), dtype=self.sparse_dtype)
+        i_sparse = sparse.csc_array(identity, dtype=self.sparse_dtype)
+        x_sparse = sparse.csc_array(sigma_x, dtype=self.sparse_dtype)
         for i in range(num_qubits):
             terms = [i_sparse] * num_qubits
             terms[i] = x_sparse
-            H_init -= functools.reduce(lambda A, B: sparse.kron(A, B, format="csr"), terms)
+            H_init -= functools.reduce(lambda A, B: sparse.kron(A, B, format=self.sparse_format), terms)
         return H_init
 
     def get_H_final(self, num_qubits, h, J):
         size = 2**num_qubits
-        diag = np.zeros(size, dtype=self.dtype)
+        diag = np.zeros(size, dtype=self.sparse_dtype)
         i_diag = np.diag(identity)
         z_diag = np.diag(sigma_z)
         for i in range(num_qubits):
@@ -46,7 +47,7 @@ def get_H_final(self, num_qubits, h, J):
                 terms[i] = z_diag
                 terms[j] = z_diag
                 diag += J[i, j] * functools.reduce(np.outer, terms).ravel()
-        H_final = sparse.dia_array((diag, [0]), shape=(size, size))
+        H_final = sparse.diags(diag, offsets=0, format=self.sparse_format)
         return H_final
 
     def run(self, bqm):