Use Eye everywhere

Author: albertomercurio

src/qobj/boolean_functions.jl

@@ -85,7 +85,7 @@ Test whether the [`QuantumObject`](@ref) ``U`` is unitary operator. This functio
 
 Note that all the keyword arguments will be passed to `Base.isapprox`.
 """
-isunitary(U::QuantumObject; kwargs...) = isoper(U) ? isapprox(U.data * U.data', I(size(U, 1)); kwargs...) : false
+isunitary(U::QuantumObject; kwargs...) = isoper(U) ? isapprox(U.data * U.data', Eye(size(U, 1)); kwargs...) : false
 
 @doc raw"""
     SciMLOperators.iscached(A::AbstractQuantumObject)

src/qobj/states.jl

@@ -149,10 +149,10 @@ The `dimensions` can be either the following types:
     If you want to keep type stability, it is recommended to use `maximally_mixed_dm(dimensions)` with `dimensions` as `Tuple` or `SVector` from [StaticArrays.jl](https://github.com/JuliaArrays/StaticArrays.jl) to keep type stability. See the [related Section](@ref doc:Type-Stability) about type stability for more details.
 """
 maximally_mixed_dm(dimensions::Int) =
-    QuantumObject(I(dimensions) / complex(dimensions), Operator(), SVector(dimensions))
+    QuantumObject(Eye(dimensions) / complex(dimensions), Operator(), SVector(dimensions))
 function maximally_mixed_dm(dimensions::Union{Dimensions,AbstractVector{Int},Tuple})
     N = prod(dimensions)
-    return QuantumObject(I(N) / complex(N), Operator(), dimensions)
+    return QuantumObject(Eye(N) / complex(N), Operator(), dimensions)
 end
 
 @doc raw"""

src/spectrum.jl

@@ -140,7 +140,7 @@ function _spectrum(
     _tr = SparseVector(D^2, [1 + n * (D + 1) for n in 0:(D-1)], ones(_complex_float_type(L), D)) # same as vec(system_identity_matrix)
     _tr_A = transpose(_tr) * spre(A).data
 
-    Id = I(D^2)
+    Id = Eye(D^2)
 
     # DO the idx = 1 case
     ω = ωList[1]

src/time_evolution/time_evolution_dynamical.jl

@@ -362,7 +362,7 @@ function dsf_mesolveProblem(
     op_l_vec = map(op -> mat2vec(get_data(op)'), op_list)
     # Create the Krylov subspace with kron(H₀.data, H₀.data) just for initialize
     expv_cache = arnoldi(kron(H₀.data, H₀.data), mat2vec(ket2dm(ψ0).data), krylov_dim)
-    dsf_identity = I(prod(H₀.dimensions))
+    dsf_identity = Eye(prod(H₀.dimensions))
     dsf_displace_cache_left = sum(op -> ScalarOperator(one(T)) * MatrixOperator(kron(op.data, dsf_identity)), op_list)
     dsf_displace_cache_left_dag =
         sum(op -> ScalarOperator(one(T)) * MatrixOperator(kron(sparse(op.data'), dsf_identity)), op_list)