From e05b77eb14c111636d6d79159d686e53239262a7 Mon Sep 17 00:00:00 2001
From: Johnnie Gray <johnniemcgray@gmail.com>
Date: Wed, 3 Jul 2024 16:20:48 -0700
Subject: [PATCH] change .A -> .toarray() for scipy 1.14

---
 docs/examples/ex_2d.ipynb                     | 4110 ++++++++++++++++-
 quimb/core.py                                 |   21 +-
 quimb/evo.py                                  |    6 +-
 .../operatorbuilder/operatorbuilder.py        |    2 +-
 quimb/gen/operators.py                        |    4 +-
 quimb/linalg/base_linalg.py                   |    4 +-
 quimb/linalg/numpy_linalg.py                  |   13 +-
 quimb/linalg/scipy_linalg.py                  |    6 +-
 quimb/tensor/tensor_1d.py                     |    3 +
 quimb/tensor/tensor_arbgeom_tebd.py           |    2 +-
 quimb/tensor/tensor_core.py                   |    3 +-
 quimb/tensor/tensor_dmrg.py                   |    4 +-
 tests/test_accel.py                           |   48 +-
 tests/test_core.py                            |   70 +-
 tests/test_evo.py                             |   24 +-
 tests/test_gen/test_operators.py              |    2 +-
 tests/test_gen/test_rand.py                   |    4 +-
 tests/test_linalg/test_approx_spectral.py     |    4 +-
 tests/test_linalg/test_base_linalg.py         |    4 +-
 tests/test_linalg/test_mpi_linalg.py          |    2 +-
 tests/test_linalg/test_rand_linalg.py         |    6 +-
 tests/test_linalg/test_slepc_linalg.py        |    6 +-
 tests/test_tensor/test_mera.py                |    2 +-
 tests/test_tensor/test_tensor_builder.py      |    2 +-
 tests/test_tensor/test_tensor_dmrg.py         |    4 +-
 25 files changed, 4227 insertions(+), 129 deletions(-)

diff --git a/docs/examples/ex_2d.ipynb b/docs/examples/ex_2d.ipynb
index e696ab89..96b7139d 100644
--- a/docs/examples/ex_2d.ipynb
+++ b/docs/examples/ex_2d.ipynb
@@ -29,12 +29,12 @@
     "\n",
     "def ham_heis_2D(n, m, j=1.0, bz=0.0, cyclic=False,\n",
     "                sparse=True):\n",
-    "    \n",
+    "\n",
     "    dims = [[2] * m] * n  # shape (n, m)\n",
-    "    \n",
+    "\n",
     "    # generate tuple of all site coordinates\n",
     "    sites = tuple(itertools.product(range(n), range(m)))\n",
-    "    \n",
+    "\n",
     "    # generate neighbouring pairs of coordinates\n",
     "    def gen_pairs():\n",
     "        for i, j, in sites:\n",
@@ -44,34 +44,34 @@
     "                yield ((i, j), (above, j))\n",
     "            if cyclic or right != 0:\n",
     "                yield ((i, j), (i, right))\n",
-    "    \n",
+    "\n",
     "    # generate all pairs of coordinates and directions\n",
     "    pairs_ss = tuple(itertools.product(gen_pairs(), 'xyz'))\n",
-    "    \n",
+    "\n",
     "    # make XX, YY and ZZ interaction from pair_s\n",
     "    #     e.g. arg ([(3, 4), (3, 5)], 'z')\n",
     "    def interactions(pair_s):\n",
     "        pair, s = pair_s\n",
     "        Sxyz = spin_operator(s, sparse=True)\n",
     "        return ikron([j * Sxyz, Sxyz], dims, inds=pair)\n",
-    "        \n",
+    "\n",
     "    # function to make Z field at ``site``\n",
     "    def fields(site):\n",
     "        Sz = spin_operator('z', sparse=True)\n",
     "        return ikron(bz * Sz, dims, inds=[site])\n",
-    "                \n",
+    "\n",
     "    # combine all terms\n",
-    "    all_terms = itertools.chain(map(interactions, pairs_ss), \n",
+    "    all_terms = itertools.chain(map(interactions, pairs_ss),\n",
     "                                map(fields, sites) if bz != 0.0 else ())\n",
     "    H = sum(all_terms)\n",
     "\n",
     "    # can improve speed of e.g. eigensolving if known to be real\n",
     "    if isreal(H):\n",
     "        H = H.real\n",
-    "    \n",
+    "\n",
     "    if not sparse:\n",
-    "        H = qarray(H.A)\n",
-    "        \n",
+    "        H = qarray(H.toarray())\n",
+    "\n",
     "    return H"
    ]
   },
@@ -203,8 +203,8 @@
    "outputs": [],
    "source": [
     "Sz = spin_operator('Z', stype='coo')\n",
-    "Sz_ij = [[ikron(Sz, dims, [(i, j)]) \n",
-    "          for j in range(m)] \n",
+    "Sz_ij = [[ikron(Sz, dims, [(i, j)])\n",
+    "          for j in range(m)]\n",
     "         for i in range(n)]"
    ]
   },
@@ -231,7 +231,1353 @@
     {
      "data": {
       "image/png": 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+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"ytick_75\">\n",
+       "     <g id=\"line2d_101\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m5896002bcb\" x=\"346.960725\" y=\"38.81929\" style=\"fill: #808080; stroke: #808080; stroke-width: 0.6\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"ytick_76\">\n",
+       "     <g id=\"line2d_102\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m5896002bcb\" x=\"346.960725\" y=\"31.539779\" style=\"fill: #808080; stroke: #808080; stroke-width: 0.6\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "    <g id=\"ytick_77\">\n",
+       "     <g id=\"line2d_103\">\n",
+       "      <g>\n",
+       "       <use xlink:href=\"#m5896002bcb\" x=\"346.960725\" y=\"16.980757\" style=\"fill: #808080; stroke: #808080; stroke-width: 0.6\"/>\n",
+       "      </g>\n",
+       "     </g>\n",
+       "    </g>\n",
+       "   </g>\n",
+       "   <g id=\"LineCollection_1\"/>\n",
+       "   <g id=\"patch_7\">\n",
+       "    <path d=\"M 333.655125 277.111219 \n",
+       "L 340.307925 277.111219 \n",
+       "L 346.960725 277.111219 \n",
+       "L 346.960725 10.999219 \n",
+       "L 340.307925 10.999219 \n",
+       "L 333.655125 10.999219 \n",
+       "L 333.655125 277.111219 \n",
+       "z\n",
+       "\" style=\"fill: none; stroke: #808080; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n",
+       "   </g>\n",
+       "  </g>\n",
+       " </g>\n",
+       " <defs>\n",
+       "  <clipPath id=\"pcae5f066e1\">\n",
+       "   <rect x=\"30.103125\" y=\"10.999219\" width=\"285.696\" height=\"266.112\"/>\n",
+       "  </clipPath>\n",
+       "  <clipPath id=\"pb277edf99e\">\n",
+       "   <rect x=\"333.655125\" y=\"10.999219\" width=\"13.3056\" height=\"266.112\"/>\n",
+       "  </clipPath>\n",
+       " </defs>\n",
+       "</svg>\n"
+      ],
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -243,8 +1589,8 @@
     }
    ],
    "source": [
-    "m_ij = [[expec(Sz_ij[i][j], gs) \n",
-    "         for j in range(m)] \n",
+    "m_ij = [[expec(Sz_ij[i][j], gs)\n",
+    "         for j in range(m)]\n",
     "        for i in range(n)]\n",
     "\n",
     "%matplotlib inline\n",
@@ -303,7 +1649,1343 @@
     {
      "data": {
       "image/png": 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@@ -315,7 +2997,7 @@
     }
    ],
    "source": [
-    "mi_ij = [[mutinf(rho_ab_ij[i][j] if (i, j) != target else \n",
+    "mi_ij = [[mutinf(rho_ab_ij[i][j] if (i, j) != target else\n",
     "                 purify(rho_ab_ij[i][j]))\n",
     "          for j in range(m)]\n",
     "         for i in range(n)]\n",
@@ -370,7 +3052,1395 @@
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       "text/plain": [
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       ]
@@ -382,7 +4452,7 @@
     }
    ],
    "source": [
-    "cy_ij = [[z_corr(rho_ab_ij[i][j] if (i, j) != target else \n",
+    "cy_ij = [[z_corr(rho_ab_ij[i][j] if (i, j) != target else\n",
     "                 purify(rho_ab_ij[i][j]))\n",
     "          for j in range(m)]\n",
     "         for i in range(n)]\n",
diff --git a/quimb/core.py b/quimb/core.py
index 3b303e2c..fa89fbef 100644
--- a/quimb/core.py
+++ b/quimb/core.py
@@ -204,6 +204,9 @@ def H(self):
         else:
             return self.transpose()
 
+    def toarray(self):
+        return np.asarray(self)
+
     @property
     def A(self):
         return np.asarray(self)
@@ -1040,7 +1043,7 @@ def quimbify(
 
     if qtype is not None:
         # Must be dense to reshape
-        data = qarray(data.A if sparse_input else data)
+        data = qarray(data.toarray() if sparse_input else data)
         if qtype in ("k", "ket"):
             data = data.reshape((prod(data.shape), 1))
         elif qtype in ("b", "bra"):
@@ -1051,7 +1054,7 @@ def quimbify(
 
     # Just cast as qarray
     elif not sparse_output:
-        data = qarray(data.A if sparse_input else data, dtype=dtype)
+        data = qarray(data.toarray() if sparse_input else data, dtype=dtype)
 
     # Check if already sparse matrix, or wanted to be one
     if sparse_output:
@@ -1828,7 +1831,7 @@ def permute(p, dims, perm):
 
     >>> IX = speye(2) & pauli('X', sparse=True)
     >>> XI = permute(IX, dims=[2, 2], perm=[1, 0])
-    >>> np.allclose(XI.A, pauli('X') & eye(2))
+    >>> np.allclose(XI.toarray(), pauli('X') & eye(2))
     True
     """
     if issparse(p):
@@ -2152,6 +2155,18 @@ def partial_trace(p, dims, keep):
 sp.coo_matrix.__and__ = kron_dispatch
 
 
+if not hasattr(sp.csr_matrix, "H"):
+    # scipy >=1.14 removed the .H attribute
+
+    def sparse_hermitian_conjugate(self):
+        return self.conjugate().transpose()
+
+    sp.csr_matrix.H = property(sparse_hermitian_conjugate)
+    sp.csc_matrix.H = property(sparse_hermitian_conjugate)
+    sp.coo_matrix.H = property(sparse_hermitian_conjugate)
+    sp.bsr_matrix.H = property(sparse_hermitian_conjugate)
+
+
 def csr_mulvec_wrap(fn):
     """Dispatch sparse csr-vector multiplication to parallel method."""
 
diff --git a/quimb/evo.py b/quimb/evo.py
index af877f40..2feb4232 100644
--- a/quimb/evo.py
+++ b/quimb/evo.py
@@ -532,7 +532,7 @@ def _setup_solved_ham(self):
             evals, evecs = self._ham
             self._method = "solve"
         except ValueError:
-            evals, evecs = eigh(self._ham.A)
+            evals, evecs = eigh(self._ham.toarray())
             self._ham = (evals, evecs)
 
         # Find initial state in energy eigenbasis at t0
@@ -578,7 +578,9 @@ def solout(t, y):
 
             self._stepper.set_solout(solout)
 
-        self._stepper.set_initial_value(self._p0.A.reshape(-1), self.t0)
+        self._stepper.set_initial_value(
+            self._p0.toarray().reshape(-1), self.t0
+        )
 
         # assign the correct update_to method
         self._update_method = self._update_to_integrate
diff --git a/quimb/experimental/operatorbuilder/operatorbuilder.py b/quimb/experimental/operatorbuilder/operatorbuilder.py
index 6878b164..eb59610e 100644
--- a/quimb/experimental/operatorbuilder/operatorbuilder.py
+++ b/quimb/experimental/operatorbuilder/operatorbuilder.py
@@ -669,7 +669,7 @@ def build_dense(self):
         operator.
         """
         A = self.build_sparse_matrix(stype="coo")
-        return A.A
+        return A.toarray()
 
     def build_local_terms(self):
         """Get a dictionary of local terms, where each key is a sorted tuple
diff --git a/quimb/gen/operators.py b/quimb/gen/operators.py
index ba5104e2..634e4622 100644
--- a/quimb/gen/operators.py
+++ b/quimb/gen/operators.py
@@ -555,7 +555,7 @@ def ham_fn(*args, stype='csr', sparse=False, **kwargs):
             H = H.real
 
         if not sparse:
-            H = qarray(H.A)
+            H = qarray(H.toarray())
         elif H.format != stype:
             H = H.asformat(stype)
 
@@ -1021,7 +1021,7 @@ def zspin_projector(n, sz=0, stype="csr", dtype=float):
 
     Examples
     --------
-    >>> zspin_projector(n=2, sz=0).A
+    >>> zspin_projector(n=2, sz=0).toarray()
     array([[0., 0.],
            [1., 0.],
            [0., 1.],
diff --git a/quimb/linalg/base_linalg.py b/quimb/linalg/base_linalg.py
index 1117133e..f5d56e58 100644
--- a/quimb/linalg/base_linalg.py
+++ b/quimb/linalg/base_linalg.py
@@ -320,9 +320,9 @@ def eigh_window(
 
     if isdense(A) or backend.upper() == "NUMPY":
         if return_vecs:
-            lk, vk = eigh(A.A if issparse(A) else A, **kwargs)
+            lk, vk = eigh(A.toarray() if issparse(A) else A, **kwargs)
         else:
-            lk = eigvalsh(A.A if issparse(A) else A, **kwargs)
+            lk = eigvalsh(A.toarray() if issparse(A) else A, **kwargs)
 
         lmin, lmax = lk[0], lk[-1]
         l_w0, l_wmin, l_wmax = _rel_window_to_abs_window(lmin, lmax, w_0, w_sz)
diff --git a/quimb/linalg/numpy_linalg.py b/quimb/linalg/numpy_linalg.py
index 2af0ef4d..c91c8011 100644
--- a/quimb/linalg/numpy_linalg.py
+++ b/quimb/linalg/numpy_linalg.py
@@ -1,5 +1,4 @@
-"""Numpy base linear algebra.
-"""
+"""Numpy base linear algebra."""
 
 import numpy as np
 import numpy.linalg as nla
@@ -188,7 +187,7 @@ def eigs_numpy(
 
     if return_vecs:
         # get all eigenpairs
-        lk, vk = eig_fn(A.A if qu.issparse(A) else A, **eig_opts)
+        lk, vk = eig_fn(A.toarray() if qu.issparse(A) else A, **eig_opts)
 
         # sort and trim according to which k we want
         sk = sort_inds(lk, method=which, sigma=sigma)[:k]
@@ -207,7 +206,7 @@ def eigs_numpy(
 
     else:
         # get all eigenvalues
-        lk = eig_fn(A.A if qu.issparse(A) else A, **eig_opts)
+        lk = eig_fn(A.toarray() if qu.issparse(A) else A, **eig_opts)
 
         # sort and trim according to which k we want
         sk = sort_inds(lk, method=which, sigma=sigma)[:k]
@@ -236,8 +235,10 @@ def svds_numpy(a, k, return_vecs=True, **_):
         Singlar value triplets.
     """
     if return_vecs:
-        uk, sk, vkt = nla.svd(a.A if qu.issparse(a) else a, compute_uv=True)
+        uk, sk, vkt = nla.svd(
+            a.toarray() if qu.issparse(a) else a, compute_uv=True
+        )
         return qu.qarray(uk[:, :k]), sk[:k], qu.qarray(vkt[:k, :])
     else:
-        sk = nla.svd(a.A if qu.issparse(a) else a, compute_uv=False)
+        sk = nla.svd(a.toarray() if qu.issparse(a) else a, compute_uv=False)
         return sk[:k]
diff --git a/quimb/linalg/scipy_linalg.py b/quimb/linalg/scipy_linalg.py
index 37ca9146..ad3d6ad9 100644
--- a/quimb/linalg/scipy_linalg.py
+++ b/quimb/linalg/scipy_linalg.py
@@ -81,7 +81,7 @@ def eigs_scipy(
 
     # avoid matrix like behaviour
     if isinstance(A, qu.qarray):
-        A = A.A
+        A = A.toarray()
 
     # project into subspace
     if P is not None:
@@ -219,7 +219,7 @@ def eigs_lobpcg(
 
     # avoid matrix like behaviour
     if isinstance(A, qu.qarray):
-        A = A.A
+        A = A.toarray()
 
     d = A.shape[0]
 
@@ -271,7 +271,7 @@ def svds_scipy(A, k=6, *, return_vecs=True, backend=None, **svds_opts):
 
     # avoid matrix like behaviour
     if isinstance(A, qu.qarray):
-        A = A.A
+        A = A.toarray()
 
     if backend is None:
         svds = spla.svds
diff --git a/quimb/tensor/tensor_1d.py b/quimb/tensor/tensor_1d.py
index 9a94c840..f66e4b4f 100644
--- a/quimb/tensor/tensor_1d.py
+++ b/quimb/tensor/tensor_1d.py
@@ -4287,6 +4287,9 @@ def to_dense(self):
 
         return T.to_dense(self.left_inds, self.right_inds)
 
+    def toarray(self):
+        return self.to_dense()
+
     @property
     def A(self):
         return self.to_dense()
diff --git a/quimb/tensor/tensor_arbgeom_tebd.py b/quimb/tensor/tensor_arbgeom_tebd.py
index ef400577..a76773e9 100644
--- a/quimb/tensor/tensor_arbgeom_tebd.py
+++ b/quimb/tensor/tensor_arbgeom_tebd.py
@@ -138,7 +138,7 @@ def _convert_from_qarray_cached(self, x):
         cache = self._op_cache["convert_from_qarray"]
         key = id(x)
         if key not in cache:
-            cache[key] = x.A
+            cache[key] = x.toarray()
         return cache[key]
 
     def _flip_cached(self, x):
diff --git a/quimb/tensor/tensor_core.py b/quimb/tensor/tensor_core.py
index 39f6d7a6..8c4c8e6d 100644
--- a/quimb/tensor/tensor_core.py
+++ b/quimb/tensor/tensor_core.py
@@ -1399,7 +1399,7 @@ def get_params(self):
 
         if isinstance(params, qarray):
             # some optimizers don't like ndarray subclasses such as qarray
-            params = params.A
+            params = params.toarray()
 
         return params
 
@@ -11079,6 +11079,7 @@ def to_dense(self, *inds_seq, to_qarray=False, **contract_opts):
             to_qarray=to_qarray,
         )
 
+    toarray = to_dense
     to_qarray = functools.partialmethod(to_dense, to_qarray=True)
 
     @functools.wraps(tensor_split)
diff --git a/quimb/tensor/tensor_dmrg.py b/quimb/tensor/tensor_dmrg.py
index 73067080..6870fb88 100644
--- a/quimb/tensor/tensor_dmrg.py
+++ b/quimb/tensor/tensor_dmrg.py
@@ -785,7 +785,7 @@ def _update_local_state_1site(self, i, direction, **compress_opts):
         loc_en, loc_gs = self.post_check(i, Neff, loc_gs, loc_en, loc_gs_old)
 
         # insert back into state and all tensor networks viewing it
-        loc_gs = loc_gs.A.reshape(dims)
+        loc_gs = loc_gs.toarray().reshape(dims)
         self._k[i].modify(data=loc_gs)
         self._b[i].modify(data=loc_gs.conj())
 
@@ -839,7 +839,7 @@ def _update_local_state_2site(self, i, direction, **compress_opts):
         loc_en, loc_gs = self.post_check(i, Neff, loc_gs, loc_en, loc_gs_old)
 
         # split the two site local groundstate
-        T_AB = Tensor(loc_gs.A.reshape(dims), uix)
+        T_AB = Tensor(loc_gs.toarray().reshape(dims), uix)
         L, R = T_AB.split(
             left_inds=uix_L,
             get="arrays",
diff --git a/tests/test_accel.py b/tests/test_accel.py
index 18ef985b..35206bf6 100644
--- a/tests/test_accel.py
+++ b/tests/test_accel.py
@@ -225,21 +225,21 @@ def test_mul_broadcast(self, mat_d, ket_d):
 
     def test_mul_sparse(self, mat_s, mat_s2):
         cq = mul(mat_s, mat_s2)
-        cn = mat_s.A * mat_s2.A
+        cn = mat_s.toarray() * mat_s2.toarray()
         assert issparse(cq)
-        assert_allclose(cq.A, cn)
-        cq = mul(mat_s2.A, mat_s)
-        cn = mat_s2.A * mat_s.A
+        assert_allclose(cq.toarray(), cn)
+        cq = mul(mat_s2.toarray(), mat_s)
+        cn = mat_s2.toarray() * mat_s.toarray()
         assert issparse(cq)
-        assert_allclose(cq.A, cn)
+        assert_allclose(cq.toarray(), cn)
 
     def test_mul_sparse_broadcast(self, mat_s, ket_d):
         ca = mul(mat_s, ket_d)
-        cn = np.multiply(mat_s.A, ket_d)
-        assert_allclose(ca.A, cn)
+        cn = np.multiply(mat_s.toarray(), ket_d)
+        assert_allclose(ca.toarray(), cn)
         ca = mul(mat_s.H, ket_d)
-        cn = np.multiply(mat_s.H.A, ket_d)
-        assert_allclose(ca.A, cn)
+        cn = np.multiply(mat_s.H.toarray(), ket_d)
+        assert_allclose(ca.toarray(), cn)
 
 
 class TestDot:
@@ -259,7 +259,7 @@ def test_dot_sparse_sparse(self, mat_s, mat_s2):
         cq = dot(mat_s, mat_s2)
         cn = mat_s @ mat_s2
         assert issparse(cq)
-        assert_allclose(cq.A, cn.A)
+        assert_allclose(cq.toarray(), cn.toarray())
 
     def test_dot_sparse_dense(self, mat_s, ket_d):
         cq = dot(mat_s, ket_d)
@@ -272,7 +272,7 @@ def test_dot_sparse_dense(self, mat_s, ket_d):
             cn = mat_s._mul_vector(ket_d)
         assert not issparse(cq)
         assert isdense(cq)
-        assert_allclose(cq.A.ravel(), cn)
+        assert_allclose(cq.toarray().ravel(), cn)
 
     def test_dot_sparse_dense_ket(self, mat_s, ket_d):
         cq = dot(mat_s, ket_d)
@@ -280,7 +280,7 @@ def test_dot_sparse_dense_ket(self, mat_s, ket_d):
         assert not issparse(cq)
         assert isdense(cq)
         assert isket(cq)
-        assert_allclose(cq.A, cn)
+        assert_allclose(cq.toarray(), cn)
 
     def test_par_dot_csr_matvec(self, mat_s, ket_d):
         x = par_dot_csr_matvec(mat_s, ket_d)
@@ -330,9 +330,9 @@ def test_ldmul_large(self):
     def test_ldmul_sparse(self, mat_s, l1d):
         assert issparse(mat_s)
         a = ldmul(l1d, mat_s)
-        b = np.diag(l1d) @ mat_s.A
+        b = np.diag(l1d) @ mat_s.toarray()
         assert issparse(a)
-        assert_allclose(a.A, b)
+        assert_allclose(a.toarray(), b)
 
     def test_rdmul_small(self, mat_d, l1d):
         a = rdmul(mat_d, l1d)
@@ -350,9 +350,9 @@ def test_rdmul_large(self):
 
     def test_rdmul_sparse(self, mat_s, l1d):
         a = rdmul(mat_s, l1d)
-        b = mat_s.A @ np.diag(l1d)
+        b = mat_s.toarray() @ np.diag(l1d)
         assert issparse(a)
-        assert_allclose(a.A, b)
+        assert_allclose(a.toarray(), b)
 
 
 class TestOuter:
@@ -412,8 +412,14 @@ def test_kron_multi_args(self, mat_d, mat_d2, mat_d3):
         )
 
     def test_kron_mixed_types(self, mat_d, mat_s):
-        assert_allclose(kron(mat_d, mat_s).A, (sp.kron(mat_d, mat_s, "csr")).A)
-        assert_allclose(kron(mat_s, mat_s).A, (sp.kron(mat_s, mat_s, "csr")).A)
+        assert_allclose(
+            kron(mat_d, mat_s).toarray(),
+            (sp.kron(mat_d, mat_s, "csr")).toarray(),
+        )
+        assert_allclose(
+            kron(mat_s, mat_s).toarray(),
+            (sp.kron(mat_s, mat_s, "csr")).toarray(),
+        )
 
 
 class TestKronSparseFormats:
@@ -476,14 +482,14 @@ def test_dense(self, mat_d):
     def test_sparse(self, mat_s):
         x = mat_s & mat_s & mat_s
         y = kronpow(mat_s, 3)
-        assert_allclose(x.A, y.A)
+        assert_allclose(x.toarray(), y.toarray())
 
     @mark.parametrize("stype", _SPARSE_FORMATS)
     def test_sparse_formats(self, stype, mat_s):
         x = mat_s & mat_s & mat_s
         y = kronpow(mat_s, 3, stype=stype)
         assert y.format == stype
-        assert_allclose(x.A, y.A)
+        assert_allclose(x.toarray(), y.toarray())
 
     @mark.parametrize("sformat_in", _SPARSE_FORMATS)
     @mark.parametrize("stype", (None,) + _SPARSE_FORMATS)
@@ -492,4 +498,4 @@ def test_sparse_formats_coo_construct(self, sformat_in, stype, mat_s):
         x = mat_s & mat_s & mat_s
         y = kronpow(mat_s, 3, stype=stype, coo_build=True)
         assert y.format == stype if stype is not None else "sformat_in"
-        assert_allclose(x.A, y.A)
+        assert_allclose(x.toarray(), y.toarray())
diff --git a/tests/test_core.py b/tests/test_core.py
index a36ee0b0..0c8fc5a7 100644
--- a/tests/test_core.py
+++ b/tests/test_core.py
@@ -129,9 +129,9 @@ def test_reshape_sparse(
         assert y.format == format_out
 
         if np.issubdtype(dtype, np.floating):
-            assert_allclose(y.A, np.real(out), atol=1e-12)
+            assert_allclose(y.toarray(), np.real(out), atol=1e-12)
         else:
-            assert_allclose(y.A, out)
+            assert_allclose(y.toarray(), out)
 
     @mark.parametrize(
         "qtype, shape, out",
@@ -154,7 +154,7 @@ def test_dense_to_sparse_format(self, qtype, shape, out, format_out):
         if format_out is None:
             format_out = "csr"
         assert y.format == format_out
-        assert_allclose(y.A, out)
+        assert_allclose(y.toarray(), out)
 
     @mark.parametrize(
         "qtype, shape", (["bra", (1, 4)], ["ket", (4, 1)], ["dop", (4, 4)])
@@ -355,7 +355,7 @@ def test_kron_ownership(self, sparse, ri, rf):
         ops = [qu.rand_matrix(d, sparse=sparse) for d in dims]
         X1 = qu.kron(*ops)[ri:rf, :]
         X2 = qu.kron(*ops, ownership=(ri, rf))
-        assert_allclose(X1.A, X2.A)
+        assert_allclose(X1.toarray(), X2.toarray())
 
 
 class Testikron:
@@ -439,11 +439,11 @@ def test_sparse(self):
         a = qu.qu(qu.rand_matrix(2), sparse=True)
         b = qu.ikron(a, [2, 2, 2], 1)  # infer sparse
         assert qu.issparse(b)
-        assert_allclose(b.A, (i & a & i).A)
+        assert_allclose(b.toarray(), (i & a & i).toarray())
         a = qu.rand_matrix(2)
         b = qu.ikron(a, [2, 2, 2], 1, sparse=True)  # explicit sparse
         assert qu.issparse(b)
-        assert_allclose(b.A, (i & a & i).A)
+        assert_allclose(b.toarray(), (i & a & i).toarray())
 
     def test_2d_simple(self):
         a = (qu.rand_matrix(2), qu.rand_matrix(2))
@@ -487,7 +487,7 @@ def test_ikron_ownership(self, sparse, ri, rf):
         Y = qu.rand_matrix(3, sparse=sparse)
         X1 = qu.ikron((X, Y), dims, (1, 3))[ri:rf, :]
         X2 = qu.ikron((X, Y), dims, (1, 3), ownership=(ri, rf))
-        assert_allclose(X1.A, X2.A)
+        assert_allclose(X1.toarray(), X2.toarray())
 
 
 class TestPermikron:
@@ -496,7 +496,7 @@ def test_dop_spread(self):
         b = qu.pkron(a, [2, 2, 2], [0, 2])
         c = (
             (a & qu.eye(2))
-            .A.reshape([2, 2, 2, 2, 2, 2])
+            .toarray().reshape([2, 2, 2, 2, 2, 2])
             .transpose([0, 2, 1, 3, 5, 4])
             .reshape([8, 8])
         )
@@ -507,7 +507,7 @@ def test_dop_reverse(self):
         b = qu.pkron(a, np.array([2, 2, 2]), [2, 0])
         c = (
             (a & qu.eye(2))
-            .A.reshape([2, 2, 2, 2, 2, 2])
+            .toarray().reshape([2, 2, 2, 2, 2, 2])
             .transpose([1, 2, 0, 4, 5, 3])
             .reshape([8, 8])
         )
@@ -518,11 +518,11 @@ def test_dop_reverse_sparse(self):
         b = qu.pkron(a, np.array([2, 2, 2]), [2, 0])
         c = (
             (a & qu.eye(2))
-            .A.reshape([2, 2, 2, 2, 2, 2])
+            .toarray().reshape([2, 2, 2, 2, 2, 2])
             .transpose([1, 2, 0, 4, 5, 3])
             .reshape([8, 8])
         )
-        assert_allclose(b.A, c)
+        assert_allclose(b.toarray(), c)
 
 
 class TestPermute:
@@ -548,15 +548,15 @@ def test_permute_sparse_ket(self):
         dims = [3, 2, 5, 4]
         a = qu.rand_ket(qu.prod(dims), sparse=True, density=0.5)
         b = qu.permute(a, dims, [3, 1, 2, 0])
-        c = qu.permute(a.A, dims, [3, 1, 2, 0])
-        assert_allclose(b.A, c)
+        c = qu.permute(a.toarray(), dims, [3, 1, 2, 0])
+        assert_allclose(b.toarray(), c)
 
     def test_permute_sparse_op(self):
         dims = [3, 2, 5, 4]
         a = qu.rand_rho(qu.prod(dims), sparse=True, density=0.5)
         b = qu.permute(a, dims, [3, 1, 2, 0])
-        c = qu.permute(a.A, dims, [3, 1, 2, 0])
-        assert_allclose(b.A, c)
+        c = qu.permute(a.toarray(), dims, [3, 1, 2, 0])
+        assert_allclose(b.toarray(), c)
 
 
 class TestPartialTraceDense:
@@ -570,10 +570,10 @@ def test_partial_trace_basic(self):
     def test_ptr_compare_to_manual(self):
         a = qu.rand_rho(2**2)
         b = qu.partial_trace(a, [2, 2], 0)
-        c = a.A.reshape([2, 2, 2, 2]).trace(axis1=1, axis2=3)
+        c = a.toarray().reshape([2, 2, 2, 2]).trace(axis1=1, axis2=3)
         assert_allclose(b, c)
         b = qu.partial_trace(a, [2, 2], 1)
-        c = a.A.reshape([2, 2, 2, 2]).trace(axis1=0, axis2=2)
+        c = a.toarray().reshape([2, 2, 2, 2]).trace(axis1=0, axis2=2)
         assert_allclose(b, c)
 
     def test_partial_trace_early_return(self):
@@ -644,11 +644,11 @@ def test_rps(self):
         )
         abc = a & b & c
         pab = qu.core._trace_lose(abc, [2, 3, 2], 2)
-        assert_allclose(pab, (a & b).A)
+        assert_allclose(pab, (a & b).toarray())
         pac = qu.core._trace_lose(abc, [2, 3, 2], 1)
-        assert_allclose(pac, (a & c).A)
+        assert_allclose(pac, (a & c).toarray())
         pbc = qu.core._trace_lose(abc, [2, 3, 2], 0)
-        assert_allclose(pbc, (b & c).A)
+        assert_allclose(pbc, (b & c).toarray())
 
     def test_bell_state(self):
         a = qu.bell_state("psi-", sparse=True)
@@ -660,19 +660,19 @@ def test_bell_state(self):
     def test_vs_ptr(self):
         a = qu.rand_rho(6, sparse=True, density=0.5)
         b = qu.core._trace_lose(a, [2, 3], 1)
-        c = qu.partial_trace(a.A, [2, 3], 0)
+        c = qu.partial_trace(a.toarray(), [2, 3], 0)
         assert_allclose(b, c)
         b = qu.core._trace_lose(a, [2, 3], 0)
-        c = qu.partial_trace(a.A, [2, 3], 1)
+        c = qu.partial_trace(a.toarray(), [2, 3], 1)
         assert_allclose(b, c)
 
     def test_vec_dense(self):
         a = qu.rand_ket(4)
         b = qu.core._trace_lose(a, [2, 2], 1)
-        c = qu.partial_trace(a.A, [2, 2], 0)
+        c = qu.partial_trace(a.toarray(), [2, 2], 0)
         assert_allclose(b, c)
         b = qu.core._trace_lose(a, [2, 2], 0)
-        c = qu.partial_trace(a.A, [2, 2], 1)
+        c = qu.partial_trace(a.toarray(), [2, 2], 1)
         assert_allclose(b, c)
 
 
@@ -685,11 +685,11 @@ def test_rps(self):
         )
         abc = a & b & c
         pc = qu.core._trace_keep(abc, [2, 3, 2], 2)
-        assert_allclose(pc, c.A)
+        assert_allclose(pc, c.toarray())
         pb = qu.core._trace_keep(abc, [2, 3, 2], 1)
-        assert_allclose(pb, b.A)
+        assert_allclose(pb, b.toarray())
         pa = qu.core._trace_keep(abc, [2, 3, 2], 0)
-        assert_allclose(pa, a.A)
+        assert_allclose(pa, a.toarray())
 
     def test_bell_state(self):
         a = qu.bell_state("psi-", sparse=True)
@@ -701,19 +701,19 @@ def test_bell_state(self):
     def test_vs_ptr(self):
         a = qu.rand_rho(6, sparse=True, density=0.5)
         b = qu.core._trace_keep(a, [2, 3], 0)
-        c = qu.partial_trace(a.A, [2, 3], 0)
+        c = qu.partial_trace(a.toarray(), [2, 3], 0)
         assert_allclose(b, c)
         b = qu.core._trace_keep(a, [2, 3], 1)
-        c = qu.partial_trace(a.A, [2, 3], 1)
+        c = qu.partial_trace(a.toarray(), [2, 3], 1)
         assert_allclose(b, c)
 
     def test_vec_dense(self):
         a = qu.rand_ket(4)
         b = qu.core._trace_keep(a, [2, 2], 0)
-        c = qu.partial_trace(a.A, [2, 2], 0)
+        c = qu.partial_trace(a.toarray(), [2, 2], 0)
         assert_allclose(b, c)
         b = qu.core._trace_keep(a, [2, 2], 1)
-        c = qu.partial_trace(a.A, [2, 2], 1)
+        c = qu.partial_trace(a.toarray(), [2, 2], 1)
         assert_allclose(b, c)
 
 
@@ -730,7 +730,7 @@ def test_partial_trace_simple_single(self):
         dims = [2, 3, 2]
         b = qu.partial_trace(a, dims, 1)
         c = (
-            a.A.reshape([*dims, *dims])
+            a.toarray().reshape([*dims, *dims])
             .trace(axis1=2, axis2=5)
             .trace(axis1=0, axis2=2)
         )
@@ -740,17 +740,17 @@ def test_partial_trace_simple_double(self):
         a = qu.rand_rho(12, sparse=True, density=0.5)
         dims = [2, 3, 2]
         b = qu.partial_trace(a, dims, [0, 2])
-        c = qu.partial_trace(a.A, dims, [0, 2])
+        c = qu.partial_trace(a.toarray(), dims, [0, 2])
         assert_allclose(b, c)
         b = qu.partial_trace(a, dims, [1, 2])
-        c = qu.partial_trace(a.A, dims, [1, 2])
+        c = qu.partial_trace(a.toarray(), dims, [1, 2])
         assert_allclose(b, c)
 
     def test_partial_trace_simple_ket(self):
         a = qu.rand_ket(12, sparse=True, density=0.5)
         dims = [2, 3, 2]
         b = qu.partial_trace(a, dims, [0, 1])
-        c = qu.partial_trace(a.A, dims, [0, 1])
+        c = qu.partial_trace(a.toarray(), dims, [0, 1])
         assert_allclose(b, c)
 
 
diff --git a/tests/test_evo.py b/tests/test_evo.py
index c04b7bcf..de00835d 100644
--- a/tests/test_evo.py
+++ b/tests/test_evo.py
@@ -95,7 +95,7 @@ def test_ket_matrix(self, psi_dot):
     def test_ket_1darray(self, psi_dot):
         psi, ham, psid = psi_dot
         foo = schrodinger_eq_ket(ham)
-        psid2 = foo(None, psi.A.reshape(-1)).reshape(-1, 1)
+        psid2 = foo(None, psi.toarray().reshape(-1)).reshape(-1, 1)
         assert_allclose(psid, psid2)
 
     def test_ket_matrix_sparse(self, spsi_dot):
@@ -107,7 +107,7 @@ def test_ket_matrix_sparse(self, spsi_dot):
     def test_ket_1darray_sparse(self, spsi_dot):
         psi, ham, psid = spsi_dot
         foo = schrodinger_eq_ket(ham)
-        psid2 = foo(None, psi.A.reshape(-1)).reshape(-1, 1)
+        psid2 = foo(None, psi.toarray().reshape(-1)).reshape(-1, 1)
         assert_allclose(psid, psid2)
 
 
@@ -115,25 +115,25 @@ class TestSchrodingerEqDop:
     def test_dop_matrix(self, rho_dot):
         rho, ham, rhod = rho_dot
         foo = schrodinger_eq_dop(ham)
-        rhod2 = foo(None, rho.A).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray()).reshape(3, 3)
         assert_allclose(rhod, rhod2)
 
     def test_dop_1darray(self, rho_dot):
         rho, ham, rhod = rho_dot
         foo = schrodinger_eq_dop(ham)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2)
 
     def test_dop_matrix_sparse(self, srho_dot):
         rho, ham, rhod = srho_dot
         foo = schrodinger_eq_dop(ham)
-        rhod2 = foo(None, rho.A).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray()).reshape(3, 3)
         assert_allclose(rhod, rhod2, atol=1e-12)
 
     def test_dop_1darray_sparse(self, srho_dot):
         rho, ham, rhod = srho_dot
         foo = schrodinger_eq_dop(ham)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2, atol=1e-12)
 
 
@@ -141,13 +141,13 @@ class TestSchrodingerEqDopVec:
     def test_dop_1darray(self, rho_dot):
         rho, ham, rhod = rho_dot
         foo = schrodinger_eq_dop_vectorized(ham)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2)
 
     def test_dop_1darray_sparse(self, srho_dot):
         rho, ham, rhod = srho_dot
         foo = schrodinger_eq_dop_vectorized(ham)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2, atol=1e-12)
 
 
@@ -161,7 +161,7 @@ def test_matrix(self, rho_dot_ls):
     def test_1darray(self, rho_dot_ls):
         rho, ham, gamma, ls, rhod = rho_dot_ls
         foo = lindblad_eq(ham, ls, gamma)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2)
 
     def test_matrix_sparse(self, srho_dot_ls):
@@ -173,7 +173,7 @@ def test_matrix_sparse(self, srho_dot_ls):
     def test_1darray_sparse(self, srho_dot_ls):
         rho, ham, gamma, ls, rhod = srho_dot_ls
         foo = lindblad_eq(ham, ls, gamma)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2)
 
 
@@ -181,13 +181,13 @@ class TestLindbladEqVec:
     def test_1darray(self, rho_dot_ls):
         rho, ham, gamma, ls, rhod = rho_dot_ls
         foo = lindblad_eq_vectorized(ham, ls, gamma)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2)
 
     def test_1darray_sparse(self, srho_dot_ls):
         rho, ham, gamma, ls, rhod = srho_dot_ls
         foo = lindblad_eq_vectorized(ham, ls, gamma)
-        rhod2 = foo(None, rho.A.reshape(-1)).reshape(3, 3)
+        rhod2 = foo(None, rho.toarray().reshape(-1)).reshape(3, 3)
         assert_allclose(rhod, rhod2)
 
 
diff --git a/tests/test_gen/test_operators.py b/tests/test_gen/test_operators.py
index 32b22229..10f5ee99 100644
--- a/tests/test_gen/test_operators.py
+++ b/tests/test_gen/test_operators.py
@@ -125,7 +125,7 @@ def test_controlled_z_dense(self):
     def test_controlled_z_sparse(self):
         cz = qu.controlled("z", sparse=True)
         assert qu.issparse(cz)
-        assert_allclose(cz.A, np.diag([1, 1, 1, -1]))
+        assert_allclose(cz.toarray(), np.diag([1, 1, 1, -1]))
 
 
 class TestGates:
diff --git a/tests/test_gen/test_rand.py b/tests/test_gen/test_rand.py
index 211d1321..d7b19240 100644
--- a/tests/test_gen/test_rand.py
+++ b/tests/test_gen/test_rand.py
@@ -142,7 +142,7 @@ def test_rand_herm_sparse(self, dtype):
         assert type(a) == sp.csr_matrix
         assert qu.isherm(a)
         assert a.dtype == dtype
-        evals = qu.eigvalsh(a.A)
+        evals = qu.eigvalsh(a.toarray())
         assert_allclose(evals.imag, [0, 0, 0], atol=1e-14)
 
 
@@ -163,7 +163,7 @@ def test_rand_pos_sparse(self, dtype):
         assert a.shape == (3, 3)
         assert type(a) == sp.csr_matrix
         assert a.dtype == dtype
-        evals = qu.eigvalsh(a.A)
+        evals = qu.eigvalsh(a.toarray())
         assert_allclose(evals.imag, [0, 0, 0], atol=1e-7)
         assert np.all(evals.real >= -1e-15)
 
diff --git a/tests/test_linalg/test_approx_spectral.py b/tests/test_linalg/test_approx_spectral.py
index 1691bba7..83c3c981 100644
--- a/tests/test_linalg/test_approx_spectral.py
+++ b/tests/test_linalg/test_approx_spectral.py
@@ -255,7 +255,7 @@ def test_approx_spectral_function_with_v0(self, dist, fn_matrix_rtol, bsz):
         actual_x = sum(fn(eigvalsh(a)))
         # check un-normalized state work properly
         v0 = neel_state(7) + neel_state(7, down_first=True)
-        v0 = v0.A.reshape(-1)
+        v0 = v0.toarray().reshape(-1)
         pos = fn == np.sqrt
         v0_opts = {"dist": dist, "orthog": True}
         approx_x = approx_spectral_function(
@@ -302,7 +302,7 @@ def test_approx_spectral_function_ptr_ppt_lin_op(
     def test_approx_spectral_subspaces_with_heis_partition(self, bsz):
         h = ham_heis(10, sparse=True)
         beta = 0.01
-        actual_Z = sum(np.exp(-beta * eigvalsh(h.A)))
+        actual_Z = sum(np.exp(-beta * eigvalsh(h.toarray())))
         approx_Z = tr_exp_approx(-beta * h, bsz=bsz)
         assert_allclose(actual_Z, approx_Z, rtol=3e-2)
 
diff --git a/tests/test_linalg/test_base_linalg.py b/tests/test_linalg/test_base_linalg.py
index 2a9eaa48..4f99d761 100644
--- a/tests/test_linalg/test_base_linalg.py
+++ b/tests/test_linalg/test_base_linalg.py
@@ -313,7 +313,7 @@ def test_zeros_dense(self, herm):
     @pytest.mark.parametrize("herm", [True, False])
     def test_eye(self, sparse, herm):
         p = qu.expm(qu.eye(2, sparse=sparse), herm=herm)
-        assert_allclose((p.A if sparse else p) / np.e, qu.eye(2))
+        assert_allclose((p.toarray() if sparse else p) / np.e, qu.eye(2))
         if sparse:
             assert isinstance(p, sp.csr_matrix)
 
@@ -338,7 +338,7 @@ def test_basic(self, sparse):
         print(hl)
         h = 1 * hl(ownership=ownership)
         h_ex = qu.ham_heis(n=4, sparse=sparse)[slice(*ownership), :]
-        assert_allclose(h.A, h_ex.A)
+        assert_allclose(h.toarray(), h_ex.toarray())
 
     @pytest.mark.parametrize("backend", ["scipy", "lobpcg"])
     def test_project_eig(self, backend):
diff --git a/tests/test_linalg/test_mpi_linalg.py b/tests/test_linalg/test_mpi_linalg.py
index c3441e6e..b431f82f 100644
--- a/tests/test_linalg/test_mpi_linalg.py
+++ b/tests/test_linalg/test_mpi_linalg.py
@@ -85,7 +85,7 @@ def test_expm_multiply(self, num_workers, bigsparsemat, big_vec):
 
         else:
             out = mfn_multiply_slepc_spawn(a, k, num_workers=num_workers)
-            al, av = eigh(a.A)
+            al, av = eigh(a.toarray())
             expected = av @ np.diag(np.exp(al)) @ av.conj().T @ k
             assert_allclose(out, expected)
 
diff --git a/tests/test_linalg/test_rand_linalg.py b/tests/test_linalg/test_rand_linalg.py
index 35cd15d5..562ca9f8 100644
--- a/tests/test_linalg/test_rand_linalg.py
+++ b/tests/test_linalg/test_rand_linalg.py
@@ -68,8 +68,8 @@ def test_rsvd(self, dtype, shape, sparse, q, p):
         assert_allclose(V @ V.conj().T, np.eye(k), rtol=1e-5, atol=1e-5)
 
         Ue, se, Ve = qu.svds(X, k)
-        opt_err = qu.norm(X.A - usv2dense(Ue, se, Ve), "fro")
-        act_err = qu.norm(X.A - usv2dense(U, s, V), "fro")
+        opt_err = qu.norm(X.toarray() - usv2dense(Ue, se, Ve), "fro")
+        act_err = qu.norm(X.toarray() - usv2dense(U, s, V), "fro")
 
         assert act_err < 1.2 * opt_err
 
@@ -123,7 +123,7 @@ def test_estimate_rank(self, dtype, shape, k_start, use_qb):
     def test_estimate_rank_lo(self, dtype, k_start, use_qb):
         X = rand_tn1d_sect(30, 10, dtype=dtype)
 
-        Ue, se, VHe = qu.svd(X.A)
+        Ue, se, VHe = qu.svd(X.toarray())
         actual_rank = sum(se > se[0] * 1e-3)
 
         k = qu.estimate_rank(
diff --git a/tests/test_linalg/test_slepc_linalg.py b/tests/test_linalg/test_slepc_linalg.py
index bbd556f6..09e23917 100644
--- a/tests/test_linalg/test_slepc_linalg.py
+++ b/tests/test_linalg/test_slepc_linalg.py
@@ -148,7 +148,7 @@ def test_exp_sparse(self):
 
         out = mfn_multiply_slepc(a, k)
 
-        al, av = eigh(a.A)
+        al, av = eigh(a.toarray())
         expected = av @ np.diag(np.exp(al)) @ av.conj().T @ k
 
         assert_allclose(out, expected)
@@ -162,7 +162,7 @@ def test_sqrt_sparse(self):
 
         out = mfn_multiply_slepc(a, k, fntype="sqrt", isherm=True)
 
-        al, av = eigh(a.A)
+        al, av = eigh(a.toarray())
         al[al < 0] = 0.0  # very small neg values spoil sqrt
         expected = av @ np.diag(np.sqrt(al)) @ av.conj().T @ k
 
@@ -261,7 +261,7 @@ def test_internal_shift_invert_precond_jd(self):
 class TestCISS:
     def test_1(self):
         a = rand_herm(100, sparse=True)
-        el, ev = eigh(a.A)
+        el, ev = eigh(a.toarray())
         which = abs(el) < 0.2
         el, ev = el[which], ev[:, which]
 
diff --git a/tests/test_tensor/test_mera.py b/tests/test_tensor/test_mera.py
index b6b75149..08b1e8d9 100644
--- a/tests/test_tensor/test_mera.py
+++ b/tests/test_tensor/test_mera.py
@@ -25,7 +25,7 @@ def test_1d_vector_methods(self):
         X = qu.spin_operator("X", sparse=True)
 
         mera = qt.MERA.rand(16)
-        meraX = mera.gate(X.A, 7)
+        meraX = mera.gate(X.toarray(), 7)
         assert mera is not meraX
         x1 = mera.H @ meraX
 
diff --git a/tests/test_tensor/test_tensor_builder.py b/tests/test_tensor/test_tensor_builder.py
index 513cb105..f8d4ccf2 100644
--- a/tests/test_tensor/test_tensor_builder.py
+++ b/tests/test_tensor/test_tensor_builder.py
@@ -73,7 +73,7 @@ def test_specials(self, var_one, var_two):
         H_mpo = HB.build_mpo(n)
         H_sps = HB.build_sparse(n)
 
-        assert_allclose(H_mpo.to_dense(), H_sps.A)
+        assert_allclose(H_mpo.to_dense(), H_sps.toarray())
 
     def test_no_default_term(self):
         N = 10
diff --git a/tests/test_tensor/test_tensor_dmrg.py b/tests/test_tensor/test_tensor_dmrg.py
index a1e81e59..e142d697 100644
--- a/tests/test_tensor/test_tensor_dmrg.py
+++ b/tests/test_tensor/test_tensor_dmrg.py
@@ -336,7 +336,7 @@ def test_variable_bond_ham(self):
         )
 
         assert_allclose(H_explicit, H_mpo.to_qarray())
-        assert_allclose(H_explicit, H_sps.A)
+        assert_allclose(H_explicit, H_sps.toarray())
 
 
 class TestDMRGX:
@@ -371,7 +371,7 @@ def test_explicit_sweeps(self):
         assert np.sum(np.abs(el - en) < 1e-12) == 1
 
         # check exactly one eigenvector is matched with high fidelity
-        ovlps = (ev.H @ k).A ** 2
+        ovlps = (ev.H @ k).toarray() ** 2
         big_ovlps = ovlps[ovlps > 1e-12]
         assert_allclose(big_ovlps, [1])