save

example/learn.jl

@@ -1,9 +1,7 @@
 using TensorQEC, TensorQEC.Yao
-using TensorInference
-qc=chain(cnot(2, 1, 2))
-cl=clifford_network(qc)
-ps = Dict([1=>TensorQEC.BoundarySpec((0.0,1.0,0.0,0.0), false),2=>TensorQEC.BoundarySpec((1.0,0.0,0.0,0.0), false)])
-qs = Dict([i=>TensorQEC.BoundarySpec((ones(Float64,4)...,), true) for i in 1:2])
-tn=generate_tensor_network(cl,ps, qs)
+# using Distributions
 
-marginals(tn)
+# function random_pauli_string(N::Int,p::Vector)
+#     values = [1, 2, 3, 4] # 1: I, 2: X, 3: Y, 4: Z
+#     return PauliString(([values[rand(Categorical(p[i]))] for i in 1:N]...,)) 
+# end

src/errorcorrect.jl

@@ -1,14 +1,17 @@
-function correction_pauli_string(qubit_num::Int, syn::Dict{Int,Bool}, prob::Dict{Int,Vector{Float64}})
-    ps = ones(Int, qubit_num)
-    for (k, v) in prob
-        @show syn[k]
-        if syn[k] #syn[k] is true, measure outcome is -1, use X or Y
-            ps[k] = (findmax(v)[2]) == 1 ? 2 : 3
-        elseif findmax(v)[2] == 2
-            ps[k] = 4
-        end
-    end
-    return PauliString(ps[1:end]...)
+function correction_pauli_string(qubit_num::Int, syn::Dict{Int, Bool}, prob::Dict{Int, Vector{Float64}})
+	ps = ones(Int, qubit_num)
+	for (k, v) in prob
+		if k ∈ keys(syn)
+			if syn[k] #syn[k] is true, measure outcome is -1, use X or Y
+				ps[k] = (findmax(v)[2]) == 1 ? 2 : 3
+			elseif findmax(v)[2] == 2
+				ps[k] = 4
+			end
+		else
+            ps[k] = [1,2,3,4][findmax(v)[2]]
+		end
+	end
+	return PauliString(ps[1:end]...)
 end
 
 

src/inferences.jl

@@ -20,5 +20,6 @@ function syndrome_inference(cl::CliffordNetwork{T}, syn::Dict{Int,Bool}, p::Vect
 	end
 	tn = generate_tensor_network(cl, ps, qs)
 	mp = marginals(tn)
-	return result = Dict([k => mp[[cl.mapped_qubits[k]]] for k in keys(syn)])
+	@show mp
+	return result = Dict([k => mp[[cl.mapped_qubits[k]]] for k in 1:n])
 end

test/errorcorrect.jl

@@ -0,0 +1,52 @@
+using Test, TensorQEC, TensorQEC.Yao
+
+
+# simple example: Z1Z3 Z2Z3 stabilizers on 3 qubits
+@testset "simple example inference and error correct" begin
+    qubit_num = 3
+    st = [PauliString((1,4,4)),PauliString((4,1,4))]
+    qc, data_qubits, code = TensorQEC.encode_stabilizers(st)
+
+    reg = join(rand_state(1), zero_state(2))  # join(qubit3, qubit2, qubit1)
+    regcopy = copy(reg)
+    apply!(reg, qc)
+    apply!(reg, put(qubit_num, 3=>X))
+
+    measure_outcome=measure_syndrome!(reg, st)
+    syn_dict=TensorQEC.generate_syndrome_dict(code,syndrome_transform(code, measure_outcome))
+
+    cl = clifford_network(qc)
+    p = fill([0.85,0.05,0.05,0.05],qubit_num)
+    pinf = syndrome_inference(cl, syn_dict, p)
+    ps_ec_phy = TensorQEC.pauli_string_map_iter(correction_pauli_string(qubit_num, syn_dict, pinf), qc)
+    apply!(reg, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
+
+    @test measure_syndrome!(reg, st) == [1,1]
+    apply!(reg, qc')
+    @test fidelity(density_matrix(reg, [data_qubits...]), density_matrix(regcopy, [data_qubits...])) ≈ 1.0
+end
+
+@testset "syndrome inference and error correct for SurfaceCode3" begin
+    qubit_num = 9
+    st = stabilizers(SurfaceCode{3}())
+    qc, data_qubits, code = TensorQEC.encode_stabilizers(st)
+
+    reg = join(zero_state(8),rand_state(1))
+    regcopy = copy(reg)
+    apply!(reg, qc)
+    apply!(reg, put(9, 3=>Z))
+    apply!(reg, put(9, 2=>X))
+
+    measure_outcome=measure_syndrome!(reg, st)
+    syn_dict=TensorQEC.generate_syndrome_dict(code,syndrome_transform(code, measure_outcome))
+
+    cl = clifford_network(qc)
+    p = fill([0.85,0.05,0.05,0.05],qubit_num)
+    pinf = syndrome_inference(cl, syn_dict, p)
+    ps_ec_phy = TensorQEC.pauli_string_map_iter(correction_pauli_string(qubit_num, syn_dict, pinf), qc)
+    apply!(reg, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
+
+    @test measure_syndrome!(reg, st) == [1,1,1,1,1,1,1,1]
+    apply!(reg, qc')
+    @test fidelity(density_matrix(reg, [9]),density_matrix(regcopy, [9])) ≈ 1.0
+end

test/inferences.jl

@@ -38,68 +38,6 @@ end
     syn=Dict([1=>true, 2=>false])
     pinf=syndrome_inference(cl, syn, p)
     @show pinf
-    @test length(pinf) == 2
+    @test length(pinf) == 3
 end
 
-# simple example: Z1Z2 Z2Z3 stabilizers on 3 qubits
-@testset "simple_example_inference" begin
-    stabilizers = [PauliString((1,4,4)),PauliString((4,4,1))]
-    qc, data_qubits, code = TensorQEC.encode_stabilizers(stabilizers)
-    reg = join(rand_state(1), zero_state(2))
-    apply!(reg, put(3, 3=>X))
-    @show reg.state
-    regcopy = copy(reg)
-    @show qc 
-    @show data_qubits
-    @show code.matrix
-
-    apply!(reg, qc)
-    @show reg.state
-    #apply!(reg, put(3, 2=>Y))
-    apply!(reg, put(3, 3=>X))
-    measure_outcome=measure_syndrome!(reg, stabilizers)
-    @show measure_outcome
-    syn=syndrome_transform(code, measure_outcome)
-    syn_dict=TensorQEC.generate_syndrome_dict(code,syn)
-
-    cl = clifford_network(qc)
-    p = fill([0.85,0.05,0.05,0.05],3)
-    pinf = syndrome_inference(cl, syn_dict, p)
-    @show pinf
-    ps_ec = correction_pauli_string(3, syn_dict, pinf)
-    ps_ec_phy = TensorQEC.pauli_string_map_iter(ps_ec, qc)
-    @show ps_ec,ps_ec_phy
-    @show reg.state
-    apply!(reg, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
-    @show reg.state
-    @test measure_syndrome!(reg, stabilizers) == [1,1]
-    @test fidelity(density_matrix(reg, [3]), density_matrix(regcopy, [3])) ≈ 1.0
-end
-
-@testset "syndrome inference for SurfaceCode3" begin
-    result = stabilizers(SurfaceCode{3}())
-    qc, data_qubits, code = TensorQEC.encode_stabilizers(result)
-
-    reg = join(zero_state(8),rand_state(1))
-    regcopy = copy(reg)
-    apply!(reg, qc)
-    apply!(reg, put(9, 5=>Y))
-    apply!(reg, put(9, 3=>Z))
-    measure_outcome=measure_syndrome!(reg, result)
-    @show measure_outcome
-    syn=syndrome_transform(code, measure_outcome)
-    syn_dict=TensorQEC.generate_syndrome_dict(code,syn)
-
-    cl = clifford_network(qc)
-    p = fill([0.94,0.02,0.02,0.02],9)
-    pinf = syndrome_inference(cl, syn_dict, p)
-    @show pinf
-    ps_ec = correction_pauli_string(9, syn_dict, pinf)
-    ps_ec_phy = TensorQEC.pauli_string_map_iter(ps_ec, qc)
-    @show 
-    apply!(reg, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
-    @test measure_syndrome!(reg, result) == [1,1,1,1,1,1,1,1]
-    apply!(reg, qc')
-    #fidelity
-    @test fidelity(density_matrix(reg, [9]),density_matrix(regcopy, [9])) ≈ 1.0
-end

test/runtests.jl

@@ -23,4 +23,8 @@ end
 
 @testset "encoder" begin
     include("encoder.jl")
-end
+end
+
+@testset "error correction" begin
+    include("errorcorrect.jl")
+end