diff --git a/examples/inference.jl b/examples/threequbits.jl
similarity index 100%
rename from examples/inference.jl
rename to examples/threequbits.jl
diff --git a/src/inferences.jl b/src/inferences.jl
index 788312c..dca75d3 100644
--- a/src/inferences.jl
+++ b/src/inferences.jl
@@ -20,5 +20,5 @@ 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 1:n])
+	return Dict([k => mp[[cl.mapped_qubits[k]]] for k in 1:n])
 end
diff --git a/test/errorcorrect.jl b/test/errorcorrect.jl
index 17ab29c..992b77b 100644
--- a/test/errorcorrect.jl
+++ b/test/errorcorrect.jl
@@ -46,9 +46,41 @@ end
 
     ps_ec_phy = TensorQEC.pauli_string_map_iter(correction_pauli_string(qubit_num, syn_dict, pinf), qc)
     @show ps_ec_phy
-    apply!(reg, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
+    apply!(reg, 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
+
+
+@testset "syndrome inference and error correct for [[7,1,3]]" begin
+    qubit_num = 7
+    pauli_string = PauliString{qubit_num}[]
+    push!(pauli_string, TensorQEC.paulistring(qubit_num, 2, (1,3,5,7)))
+    push!(pauli_string, TensorQEC.paulistring(qubit_num, 2, (2,3,6,7)))
+    push!(pauli_string, TensorQEC.paulistring(qubit_num, 2, (3,4,5,6,)))
+    push!(pauli_string, TensorQEC.paulistring(qubit_num, 4, (1,3,5,7)))
+    push!(pauli_string, TensorQEC.paulistring(qubit_num, 4, (2,3,6,7)))
+    push!(pauli_string, TensorQEC.paulistring(qubit_num, 4, (3,4,5,6,)))
+    qc, data_qubits, code = TensorQEC.encode_stabilizers(pauli_string)
+    reg = join(zero_state(6),rand_state(1))
+    regcopy = copy(reg)
+    apply!(reg, qc)
+    apply!(reg, put(qubit_num, 4=>X))
+
+    measure_outcome=measure_syndrome!(reg, pauli_string)
+    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)
+    @show ps_ec_phy
+    apply!(reg, ps_ec_phy)
+
+    @test measure_syndrome!(reg, pauli_string) == [1,1,1,1,1,1]
+    apply!(reg, qc')
+    @test fidelity(density_matrix(reg, [qubit_num]),density_matrix(regcopy, [qubit_num])) ≈ 1.0
 end
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