20861084561

Committed 09 Jan 2026 06:07PM UTC coverage: 75.516% (-0.6%) from 76.078%

Build # 20861084561

Build Type

Pull #107

github

Committed by

lpawela

Commit Message

rmeove old julia versions in CI

Pull Request Pull Request #107: SCS compat problems solution

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100.0

/src/randomqobjects.jl

export HaarKet, HilbertSchmidtStates, ChoiJamiolkowskiMatrices,
    HaarPOVM, WishartPOVM, VonNeumannPOVM


struct HaarKet{β} <: QIContinuousMatrixDistribution
    d::Int
end

HaarKet(d::Int) = HaarKet{2}(d)

function rand(rng::AbstractRNG, h::HaarKet{1})
    ψ = randn(rng, h.d)
    renormalize!(ψ)
    ψ
 end

 function rand(rng::AbstractRNG, h::HaarKet{2})
     ψ = randn(rng, h.d) + 1im * randn(rng, h.d)
     renormalize!(ψ)
     ψ
  end

# Random mixed states
struct HilbertSchmidtStates{β, K} <: QIContinuousMatrixDistribution
    w::WishartEnsemble
    d::Int

    function HilbertSchmidtStates{β, K}(d::Int) where {β, K}
        w = WishartEnsemble{β, K}(d)
        new(w, w.d)
    end
end
HilbertSchmidtStates{β}(d::Int) where β = HilbertSchmidtStates{β, 1}(d)
HilbertSchmidtStates(d::Int) = HilbertSchmidtStates{2, 1}(d)

function rand(rng::AbstractRNG, hs::HilbertSchmidtStates{β, K}) where {β, K}
    ρ = rand(rng, hs.w)
    renormalize!(ρ)
    ρ
end

#Random channels
struct ChoiJamiolkowskiMatrices{β, K} <: QIContinuousMatrixDistribution
    w::WishartEnsemble
    idim::Int
    odim::Int

    function ChoiJamiolkowskiMatrices{β, K}(idim::Int, odim::Int)  where {β, K}
        w = WishartEnsemble{β, K}(idim * odim)
        new(w, idim, odim)
    end
end

function ChoiJamiolkowskiMatrices{β}(idim::Int, odim::Int) where β
    ChoiJamiolkowskiMatrices{β, 1}(idim, odim)
end

function ChoiJamiolkowskiMatrices{β}(d::Int) where β
    ChoiJamiolkowskiMatrices{β}(d, d)
end

function ChoiJamiolkowskiMatrices(idim::Int, odim::Int)
    ChoiJamiolkowskiMatrices{2}(idim, odim)
end

function ChoiJamiolkowskiMatrices(d::Int)
    ChoiJamiolkowskiMatrices(d, d)
end

function rand(rng::AbstractRNG, c::ChoiJamiolkowskiMatrices{β, K}) where {β, K}
    z = rand(rng, c.w)
    y = ptrace(z, [c.odim, c.idim], [1])
    sy = funcmh!(x -> 1 / sqrt(x), y)
    onesy = Matrix(I, c.odim, c.odim) ⊗ sy # onesy = eye(c.odim) ⊗ sy
    DynamicalMatrix(onesy * z * onesy, c.idim, c.odim)
end

# Random POVMs implemented according to
# https://arxiv.org/pdf/1902.04751.pdf
abstract type AbstractHaarPOVM <: QIContinuousMatrixDistribution
end

struct HaarPOVM{N} <: AbstractHaarPOVM
    idim::Int
    odim::Int
    c::HaarIsometry

    function HaarPOVM{N}(idim::Int, odim::Int) where N
        c = HaarIsometry(idim::Int, N*odim::Int)
        new(idim, odim, c)
    end
end
# N controls the rank (mixedness) of the effects, N=1 gives rank-one effects
HaarPOVM(idim::Int, odim::Int) = HaarPOVM{1}(idim, odim)

#this should use slicing of V
function rand(rng::AbstractRNG, c::HaarPOVM{N}) where N
    V = rand(rng, c.c)
    POVMMeasurement([V'*(ketbra(i, i, c.odim) ⊗ 𝕀(N))*V for i=1:c.odim])
end

struct VonNeumannPOVM <: AbstractHaarPOVM
    d::Int
    c::CUE

    function VonNeumannPOVM(d::Int)
        c = CUE(d)
        new(d, c)
    end
end

function rand(rng::AbstractRNG, c::VonNeumannPOVM)
    V = rand(rng, c.c)
    POVMMeasurement([proj(V[:, i]) for i=1:c.d])
end

struct WishartPOVM{V} <: QIContinuousMatrixDistribution
    idim::Int
    odim::Int
    c::Vector{WishartEnsemble}

    function WishartPOVM{V}(idim::Int) where V
        odim = length(V)
        c = [WishartEnsemble{2, v}(idim) for v=V]
        new(idim, odim, c)
    end
end

function WishartPOVM(idim::Int, odim::Int, K::Real=1)
    V = Tuple(round.(Int, K .* ones(odim)))
    WishartPOVM{V}(idim)
end

function rand(rng::AbstractRNG, c::WishartPOVM)
    Ws = map(x->rand(rng, x), c.c)
    S = sum(Ws)
    Ssq = funcmh!(x->1/sqrt(x), S)
    POVMMeasurement([Ssq * W * Ssq for W=Ws])
end

1	export HaarKet, HilbertSchmidtStates, ChoiJamiolkowskiMatrices,
2	HaarPOVM, WishartPOVM, VonNeumannPOVM
3
4
5	struct HaarKet{β} <: QIContinuousMatrixDistribution
6	d::Int	9✔
7	end
8
9	HaarKet(d::Int) = HaarKet{2}(d)	3✔
10
11	function rand(rng::AbstractRNG, h::HaarKet{1})	3✔
12	ψ = randn(rng, h.d)	13✔
13	renormalize!(ψ)	21✔
UNCOV 14	ψ	2✔
15	end
16
17	function rand(rng::AbstractRNG, h::HaarKet{2})	6✔
18	ψ = randn(rng, h.d) + 1im * randn(rng, h.d)	26✔
19	renormalize!(ψ)	42✔
UNCOV 20	ψ	4✔
21	end
22
23	# Random mixed states
24	struct HilbertSchmidtStates{β, K} <: QIContinuousMatrixDistribution
25	w::WishartEnsemble
26	d::Int
27
28	function HilbertSchmidtStates{β, K}(d::Int) where {β, K}	16✔
29	w = WishartEnsemble{β, K}(d)	18✔
30	new(w, w.d)	18✔
31	end
32	end
33	HilbertSchmidtStates{β}(d::Int) where β = HilbertSchmidtStates{β, 1}(d)	3✔
34	HilbertSchmidtStates(d::Int) = HilbertSchmidtStates{2, 1}(d)	3✔
35
36	function rand(rng::AbstractRNG, hs::HilbertSchmidtStates{β, K}) where {β, K}	6✔
37	ρ = rand(rng, hs.w)	6✔
38	renormalize!(ρ)	6✔
UNCOV 39	ρ	4✔
40	end
41
42	#Random channels
43	struct ChoiJamiolkowskiMatrices{β, K} <: QIContinuousMatrixDistribution
44	w::WishartEnsemble
45	idim::Int
46	odim::Int
47
48	function ChoiJamiolkowskiMatrices{β, K}(idim::Int, odim::Int) where {β, K}	30✔
49	w = WishartEnsemble{β, K}(idim * odim)	36✔
50	new(w, idim, odim)	36✔
51	end
52	end
53
54	function ChoiJamiolkowskiMatrices{β}(idim::Int, odim::Int) where β	13✔
55	ChoiJamiolkowskiMatrices{β, 1}(idim, odim)	18✔
56	end
57
58	function ChoiJamiolkowskiMatrices{β}(d::Int) where β	3✔
59	ChoiJamiolkowskiMatrices{β}(d, d)	3✔
60	end
61
62	function ChoiJamiolkowskiMatrices(idim::Int, odim::Int)	11✔
63	ChoiJamiolkowskiMatrices{2}(idim, odim)	12✔
64	end
65
66	function ChoiJamiolkowskiMatrices(d::Int)	3✔
67	ChoiJamiolkowskiMatrices(d, d)	3✔
68	end
69
70	function rand(rng::AbstractRNG, c::ChoiJamiolkowskiMatrices{β, K}) where {β, K}	12✔
71	z = rand(rng, c.w)	12✔
72	y = ptrace(z, [c.odim, c.idim], [1])	24✔
73	sy = funcmh!(x -> 1 / sqrt(x), y)	60✔
74	onesy = Matrix(I, c.odim, c.odim) ⊗ sy # onesy = eye(c.odim) ⊗ sy	12✔
75	DynamicalMatrix(onesy * z * onesy, c.idim, c.odim)	12✔
76	end
77
78	# Random POVMs implemented according to
79	# https://arxiv.org/pdf/1902.04751.pdf
80	abstract type AbstractHaarPOVM <: QIContinuousMatrixDistribution
81	end
82
83	struct HaarPOVM{N} <: AbstractHaarPOVM
84	idim::Int
85	odim::Int
86	c::HaarIsometry
87
88	function HaarPOVM{N}(idim::Int, odim::Int) where N	4✔
89	c = HaarIsometry(idim::Int, N*odim::Int)	9✔
90	new(idim, odim, c)	3✔
91	end
92	end
93	# N controls the rank (mixedness) of the effects, N=1 gives rank-one effects
94	HaarPOVM(idim::Int, odim::Int) = HaarPOVM{1}(idim, odim)	6✔
95
96	#this should use slicing of V
97	function rand(rng::AbstractRNG, c::HaarPOVM{N}) where N	2✔
98	V = rand(rng, c.c)	3✔
99	POVMMeasurement([V'(ketbra(i, i, c.odim) ⊗ 𝕀(N))V for i=1:c.odim])	3✔
100	end
101
102	struct VonNeumannPOVM <: AbstractHaarPOVM
103	d::Int
104	c::CUE
105
106	function VonNeumannPOVM(d::Int)	3✔
107	c = CUE(d)	3✔
108	new(d, c)	3✔
109	end
110	end
111
112	function rand(rng::AbstractRNG, c::VonNeumannPOVM)	2✔
113	V = rand(rng, c.c)	3✔
114	POVMMeasurement([proj(V[:, i]) for i=1:c.d])	3✔
115	end
116
117	struct WishartPOVM{V} <: QIContinuousMatrixDistribution
118	idim::Int
119	odim::Int
120	c::Vector{WishartEnsemble}
121
122	function WishartPOVM{V}(idim::Int) where V	3✔
123	odim = length(V)	3✔
124	c = [WishartEnsemble{2, v}(idim) for v=V]	3✔
125	new(idim, odim, c)	3✔
126	end
127	end
128
129	function WishartPOVM(idim::Int, odim::Int, K::Real=1)	6✔
130	V = Tuple(round.(Int, K .* ones(odim)))	6✔
131	WishartPOVM{V}(idim)	3✔
132	end
133
134	function rand(rng::AbstractRNG, c::WishartPOVM)	3✔
135	Ws = map(x->rand(rng, x), c.c)	12✔
136	S = sum(Ws)	3✔
137	Ssq = funcmh!(x->1/sqrt(x), S)	9✔
138	POVMMeasurement([Ssq * W * Ssq for W=Ws])	3✔
139	end

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