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#### Brief description of what is fixed or changed
`TensorProduct` doesn't identify `ImmutableMatrix` (or any other matrix class that is not derived from mutable `Matrix`) as a matrix:
```
from sympy import *
from sympy.physics.quantum.tensorproduct import TensorProduct
X = Matrix([[0, 1], [1, 0]])
TensorProduct(X, X)
# displays full 4 x 4 matrix with all 1's on anti-diagonal
Xi = ImmutableMatrix([[0, 1], [1, 0]])
TensorProduct(Xi, Xi)
# displays unevaluated Xi * Xi
```
The reason is that `TensorProduct.__new__()`  checks if the arguments are of type `Matrix` instead of `MatrixBase`. `Matrix` is mutable, so no immutable matrix type inherits from `Matrix`, and immutable matrices are not passed on to `matrix_tensor_product()`. This clearly is a typo, as in `sympy.physics.quantum.matrixutils.matrix_tensor_product()`, `sympy.matrices.kronecker.matrix_kronecker_product()` and any other function e.g. in `matrixutils.py` the code checks for `isinstance(m, MatrixBase)`, so they work for mutable and immutable matrices alike.
However, in the testfile test_tensorproduct.py:25 the original author explicitly tests that `SparseMatrix` should not evaluate - for unknown reason, since `scipy_sparse_matrix` type _is accepted_ for evaluation in TensorProduct. So in order to avoid further implications and also to exclude symbolic matrices like `Identity()` which `matrix_kronecker_product()` cannot handle anyway, I propose to accept `ImmutableMatrix` in addition to `Matrix` instead of all `MatrixBase`.

#### Other comments
It's important to use ImmutableMatrix (or MatrixExpr in general) in sympy instead of mutable matrices because the sympy cache cannot work with mutable expressions and this may lead to random errors. 

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* physics.quantum
  * TensorProduct now processes immutable matrices and mutable matrices alike. Fixed a bug that TensorProduct didn't compute the result matrix if the argument matrices were immutable.
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