diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a33b812fd0..207171eeaa 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -7,7 +7,7 @@ exclude: |
     )$
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.3.0
+    rev: v4.4.0
     hooks:
       - id: debug-statements
         exclude: |
@@ -25,9 +25,10 @@ repos:
       - id: black
         language_version: python3
   - repo: https://github.com/pycqa/flake8
-    rev: 5.0.4
+    rev: 6.0.0
     hooks:
       - id: flake8
+        language_version: python39
   - repo: https://github.com/pycqa/isort
     rev: 5.10.1
     hooks:
@@ -47,7 +48,7 @@ repos:
           )$
         args: ['--in-place', '--remove-all-unused-imports', '--remove-unused-variable']
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v0.982
+    rev: v0.991
     hooks:
       - id: mypy
         additional_dependencies:
diff --git a/aesara/link/numba/dispatch/__init__.py b/aesara/link/numba/dispatch/__init__.py
index 31a0467483..11f47aa8f9 100644
--- a/aesara/link/numba/dispatch/__init__.py
+++ b/aesara/link/numba/dispatch/__init__.py
@@ -13,5 +13,6 @@
 import aesara.link.numba.dispatch.random
 import aesara.link.numba.dispatch.elemwise
 import aesara.link.numba.dispatch.scan
+import aesara.link.numba.dispatch.sparse
 
 # isort: on
diff --git a/aesara/link/numba/dispatch/basic.py b/aesara/link/numba/dispatch/basic.py
index b6ee96436e..bd7f1a9c79 100644
--- a/aesara/link/numba/dispatch/basic.py
+++ b/aesara/link/numba/dispatch/basic.py
@@ -371,6 +371,7 @@ def _numba_funcify(
     A `Callable` that can be JIT-compiled in Numba using `numba.jit`.
 
     """
+    raise NotImplementedError()
 
 
 @_numba_funcify.register(Op)
diff --git a/aesara/link/numba/dispatch/sparse.py b/aesara/link/numba/dispatch/sparse.py
new file mode 100644
index 0000000000..d07e029501
--- /dev/null
+++ b/aesara/link/numba/dispatch/sparse.py
@@ -0,0 +1,142 @@
+import scipy as sp
+import scipy.sparse
+from numba.core import cgutils, types
+from numba.extending import (
+    NativeValue,
+    box,
+    make_attribute_wrapper,
+    models,
+    register_model,
+    typeof_impl,
+    unbox,
+)
+
+
+class CSMatrixType(types.Type):
+    """A Numba `Type` modeled after the base class `scipy.sparse.compressed._cs_matrix`."""
+
+    name: str
+    instance_class: type
+
+    def __init__(self, dtype):
+        self.dtype = dtype
+        self.data = types.Array(dtype, 1, "A")
+        self.indices = types.Array(types.int32, 1, "A")
+        self.indptr = types.Array(types.int32, 1, "A")
+        self.shape = types.UniTuple(types.int64, 2)
+        super().__init__(self.name)
+
+
+make_attribute_wrapper(CSMatrixType, "data", "data")
+make_attribute_wrapper(CSMatrixType, "indices", "indices")
+make_attribute_wrapper(CSMatrixType, "indptr", "indptr")
+make_attribute_wrapper(CSMatrixType, "shape", "shape")
+
+
+class CSRMatrixType(CSMatrixType):
+    name = "csr_matrix"
+
+    @staticmethod
+    def instance_class(data, indices, indptr, shape):
+        return sp.sparse.csr_matrix((data, indices, indptr), shape, copy=False)
+
+
+class CSCMatrixType(CSMatrixType):
+    name = "csc_matrix"
+
+    @staticmethod
+    def instance_class(data, indices, indptr, shape):
+        return sp.sparse.csc_matrix((data, indices, indptr), shape, copy=False)
+
+
+@typeof_impl.register(sp.sparse.csc_matrix)
+def typeof_csc_matrix(val, c):
+    data = typeof_impl(val.data, c)
+    return CSCMatrixType(data.dtype)
+
+
+@typeof_impl.register(sp.sparse.csr_matrix)
+def typeof_csr_matrix(val, c):
+    data = typeof_impl(val.data, c)
+    return CSRMatrixType(data.dtype)
+
+
+@register_model(CSRMatrixType)
+class CSRMatrixModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ("data", fe_type.data),
+            ("indices", fe_type.indices),
+            ("indptr", fe_type.indptr),
+            ("shape", fe_type.shape),
+        ]
+        super().__init__(dmm, fe_type, members)
+
+
+@register_model(CSCMatrixType)
+class CSCMatrixModel(models.StructModel):
+    def __init__(self, dmm, fe_type):
+        members = [
+            ("data", fe_type.data),
+            ("indices", fe_type.indices),
+            ("indptr", fe_type.indptr),
+            ("shape", fe_type.shape),
+        ]
+        super().__init__(dmm, fe_type, members)
+
+
+@unbox(CSCMatrixType)
+@unbox(CSRMatrixType)
+def unbox_matrix(typ, obj, c):
+
+    struct_ptr = cgutils.create_struct_proxy(typ)(c.context, c.builder)
+
+    data = c.pyapi.object_getattr_string(obj, "data")
+    indices = c.pyapi.object_getattr_string(obj, "indices")
+    indptr = c.pyapi.object_getattr_string(obj, "indptr")
+    shape = c.pyapi.object_getattr_string(obj, "shape")
+
+    struct_ptr.data = c.unbox(typ.data, data).value
+    struct_ptr.indices = c.unbox(typ.indices, indices).value
+    struct_ptr.indptr = c.unbox(typ.indptr, indptr).value
+    struct_ptr.shape = c.unbox(typ.shape, shape).value
+
+    c.pyapi.decref(data)
+    c.pyapi.decref(indices)
+    c.pyapi.decref(indptr)
+    c.pyapi.decref(shape)
+
+    is_error_ptr = cgutils.alloca_once_value(c.builder, cgutils.false_bit)
+    is_error = c.builder.load(is_error_ptr)
+
+    res = NativeValue(struct_ptr._getvalue(), is_error=is_error)
+
+    return res
+
+
+@box(CSCMatrixType)
+@box(CSRMatrixType)
+def box_matrix(typ, val, c):
+    struct_ptr = cgutils.create_struct_proxy(typ)(c.context, c.builder, value=val)
+
+    data_obj = c.box(typ.data, struct_ptr.data)
+    indices_obj = c.box(typ.indices, struct_ptr.indices)
+    indptr_obj = c.box(typ.indptr, struct_ptr.indptr)
+    shape_obj = c.box(typ.shape, struct_ptr.shape)
+
+    c.pyapi.incref(data_obj)
+    c.pyapi.incref(indices_obj)
+    c.pyapi.incref(indptr_obj)
+    c.pyapi.incref(shape_obj)
+
+    cls_obj = c.pyapi.unserialize(c.pyapi.serialize_object(typ.instance_class))
+    obj = c.pyapi.call_function_objargs(
+        cls_obj, (data_obj, indices_obj, indptr_obj, shape_obj)
+    )
+
+    c.pyapi.decref(data_obj)
+    c.pyapi.decref(indices_obj)
+    c.pyapi.decref(indptr_obj)
+    c.pyapi.decref(shape_obj)
+
+    return obj
diff --git a/aesara/link/vm.py b/aesara/link/vm.py
index e1cefb33b5..e79e9ebbb4 100644
--- a/aesara/link/vm.py
+++ b/aesara/link/vm.py
@@ -1056,7 +1056,7 @@ def make_vm(
                 callback=self.callback,
                 callback_input=self.callback_input,
             )
-        elif self.use_cloop and CVM:
+        elif self.use_cloop and CVM is not None:
 
             # create a map from nodes to ints and vars to ints
             nodes_idx = {}
diff --git a/aesara/scan/op.py b/aesara/scan/op.py
index 24e0e867a7..4914e041fe 100644
--- a/aesara/scan/op.py
+++ b/aesara/scan/op.py
@@ -54,6 +54,7 @@
 import numpy as np
 
 import aesara
+import aesara.link.utils as link_utils
 from aesara import tensor as at
 from aesara.compile.builders import construct_nominal_fgraph, infer_shape
 from aesara.compile.function.pfunc import pfunc
@@ -75,7 +76,6 @@
 from aesara.graph.utils import InconsistencyError, MissingInputError
 from aesara.link.c.basic import CLinker
 from aesara.link.c.exceptions import MissingGXX
-from aesara.link.utils import raise_with_op
 from aesara.printing import op_debug_information
 from aesara.scan.utils import ScanProfileStats, Validator, forced_replace, safe_new
 from aesara.tensor.basic import as_tensor_variable
@@ -1629,7 +1629,7 @@ def p(node, inputs, outputs):
                     if hasattr(self.fn.vm, "position_of_error") and hasattr(
                         self.fn.vm, "thunks"
                     ):
-                        raise_with_op(
+                        link_utils.raise_with_op(
                             self.fn.maker.fgraph,
                             self.fn.vm.nodes[self.fn.vm.position_of_error],
                             self.fn.vm.thunks[self.fn.vm.position_of_error],
@@ -1932,7 +1932,7 @@ def perform(self, node, inputs, output_storage, params=None):
                     # done by raise_with_op is not implemented in C.
                     if hasattr(vm, "thunks"):
                         # For the CVM
-                        raise_with_op(
+                        link_utils.raise_with_op(
                             self.fn.maker.fgraph,
                             vm.nodes[vm.position_of_error],
                             vm.thunks[vm.position_of_error],
@@ -1942,7 +1942,7 @@ def perform(self, node, inputs, output_storage, params=None):
                         # We don't have access from python to all the
                         # temps values So for now, we just don't print
                         # the extra shapes/strides info
-                        raise_with_op(
+                        link_utils.raise_with_op(
                             self.fn.maker.fgraph, vm.nodes[vm.position_of_error]
                         )
                 else:
@@ -3427,7 +3427,7 @@ def profile_printer(
             )
 
 
-@op_debug_information.register(Scan)  # type: ignore[has-type]
+@op_debug_information.register(Scan)
 def _op_debug_information_Scan(op, node):
     from typing import Sequence
 
diff --git a/tests/link/numba/test_sparse.py b/tests/link/numba/test_sparse.py
new file mode 100644
index 0000000000..af49752f3b
--- /dev/null
+++ b/tests/link/numba/test_sparse.py
@@ -0,0 +1,40 @@
+import numba
+import numpy as np
+import scipy as sp
+
+# Load Numba customizations
+import aesara.link.numba.dispatch.sparse  # noqa: F401
+
+
+def test_sparse_unboxing():
+    @numba.njit
+    def test_unboxing(x, y):
+        return x.shape, y.shape
+
+    x_val = sp.sparse.csr_matrix(np.eye(100))
+    y_val = sp.sparse.csc_matrix(np.eye(101))
+
+    res = test_unboxing(x_val, y_val)
+
+    assert res == (x_val.shape, y_val.shape)
+
+
+def test_sparse_boxing():
+    @numba.njit
+    def test_boxing(x, y):
+        return x, y
+
+    x_val = sp.sparse.csr_matrix(np.eye(100))
+    y_val = sp.sparse.csc_matrix(np.eye(101))
+
+    res_x_val, res_y_val = test_boxing(x_val, y_val)
+
+    assert np.array_equal(res_x_val.data, x_val.data)
+    assert np.array_equal(res_x_val.indices, x_val.indices)
+    assert np.array_equal(res_x_val.indptr, x_val.indptr)
+    assert res_x_val.shape == x_val.shape
+
+    assert np.array_equal(res_y_val.data, y_val.data)
+    assert np.array_equal(res_y_val.indices, y_val.indices)
+    assert np.array_equal(res_y_val.indptr, y_val.indptr)
+    assert res_y_val.shape == y_val.shape