save

Makefile

@@ -5,16 +5,23 @@ default: init test
 init:
 	$(JL) -e 'using Pkg; Pkg.instantiate(); Pkg.precompile()'
 init-docs:
-	$(JL) -e 'using Pkg; Pkg.instantiate(); Pkg.precompile()'
+	$(JL) -e 'using Pkg; Pkg.activate("docs"); Pkg.develop(path="."); Pkg.instantiate(); Pkg.precompile()'
+init-examples:
+	$(JL) -e 'using Pkg; Pkg.activate("examples"); Pkg.develop(path="."); Pkg.instantiate(); Pkg.precompile()'
 
 update:
 	$(JL) -e 'using Pkg; Pkg.update(); Pkg.precompile()'
 update-docs:
 	$(JL) -e 'using Pkg; Pkg.activate("docs"); Pkg.update(); Pkg.precompile()'
+update-examples:
+	$(JL) -e 'using Pkg; Pkg.activate("examples"); Pkg.update(); Pkg.precompile()'
 
 test:
 	$(JL) -e 'using Pkg; Pkg.test()'
 
+example-%:
+	$(JL) -e 'using Pkg; Pkg.activate("examples"); include("examples/$*.jl")'
+
 coverage:
 	$(JL) -e 'using Pkg; Pkg.test(; coverage=true)'
 

README.md

@@ -4,3 +4,14 @@
 [![Dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://nzy1997.github.io/TensorQEC.jl/dev/)
 [![CI](https://github.com/nzy1997/TensorQEC.jl/actions/workflows/CI.yml/badge.svg)](https://github.com/nzy1997/TensorQEC.jl/actions/workflows/CI.yml)
 [![Coverage](https://codecov.io/gh/nzy1997/TensorQEC.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/nzy1997/TensorQEC.jl)
+
+## Examples
+To install dependencies, please open a terminal and type
+```bash
+make init-examples
+```
+
+Example files are located in the `examples` folder. Consider the example `example-inference.jl`, to run it, please open a terminal and type
+```bash
+make example-inference
+```

examples/Project.toml

@@ -0,0 +1,2 @@
+[deps]
+TensorQEC = "0500ac79-7fb5-4262-aaea-37bb1845d1ef"

examples/inference.jl

@@ -0,0 +1,46 @@
+using TensorQEC, TensorQEC.Yao
+# define the stabilizers
+qubit_num = 3
+st = [PauliString((1,4,4)),PauliString((4,1,4))]
+@info "stabilizers: $st"
+
+# Generate the encoding circuits of the stabilizers
+qc, data_qubits, code = encode_stabilizers(st)
+@info "encoding circuits: $qc, data qubits: $data_qubits"
+
+# Create a quantum register. Qubits in "data_qubits" are randomly initilized, and the rest ancilla qubits are in the |0> state.
+reg = join(rand_state(1), zero_state(2))  # join(qubit3, qubit2, qubit1)
+# Apply the encoding circuits.
+regcopy = copy(reg)
+apply!(reg, qc)
+
+# Apply a X error on the third qubit
+apply!(reg, put(qubit_num, 3=>X))
+@info "applied X error on the third qubit"
+
+# Measure the syndrome
+measure_outcome=measure_syndrome!(reg, st)
+@info "measured syndrome: $measure_outcome"
+
+# Generate the syndrome dictionary
+syn_dict=TensorQEC.generate_syndrome_dict(code,syndrome_transform(code, measure_outcome))
+
+# Generate the tensor network for syndrome inference
+cl = clifford_network(qc)
+p = fill([0.85,0.05,0.05,0.05],qubit_num)
+pinf = syndrome_inference(cl, syn_dict, p)
+@info "inferred error probability: $pinf"
+
+# Generate the Pauli string for error correction
+ps_ec_phy = TensorQEC.pauli_string_map_iter(correction_pauli_string(qubit_num, syn_dict, pinf), qc)
+@info "Pauli string for error correction: $ps_ec_phy"
+
+# Apply the error correction
+apply!(reg, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
+
+# Measure the syndrome after error correction
+syndrome_result = measure_syndrome!(reg, st)
+@info "measured syndrome: $syndrome_result"
+apply!(reg, qc')
+fidelity_after = fidelity(density_matrix(reg, [data_qubits...]), density_matrix(regcopy, [data_qubits...]))
+@info "fidelity after error correction: $fidelity_after"

src/encoder.jl

@@ -138,8 +138,8 @@ struct SurfaceCode{D} end
 #           |       |  /
 #           ∘---9---/
 # 8 "∘" represent 8 stableizers.
-# XXtype: 24, 1235, 5789, 68
-# ZZtype: 13, 2457, 3568, 79
+# X type: 24, 1235, 5789, 68
+# Z type: 13, 2457, 3568, 79
 
 function stabilizers(::SurfaceCode{3})
     nq=9

src/inferences.jl

@@ -20,6 +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)
-	@show mp
 	return result = Dict([k => mp[[cl.mapped_qubits[k]]] for k in 1:n])
 end

src/tensornetwork.jl

@@ -80,7 +80,7 @@ function generate_tensor_network(cl::CliffordNetwork{T}, ps::Dict{Int, BoundaryS
 	for (k, v) in qs
 		nvars = _add_boundary!(cl.mapped_qubits, v, k, factors, cards, mars, nvars)
 	end
-	@show mars
+	# @show mars
 	return TensorNetworkModel(
 		1:nvars,
 		cards,

test/errorcorrect.jl

@@ -19,6 +19,7 @@ using Test, TensorQEC, TensorQEC.Yao
     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, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
 
     @test measure_syndrome!(reg, st) == [1,1]
@@ -34,7 +35,7 @@ end
     reg = join(zero_state(8),rand_state(1))
     regcopy = copy(reg)
     apply!(reg, qc)
-    apply!(reg, put(9, 3=>Z))
+    apply!(reg, put(9, 3=>X))
     apply!(reg, put(9, 2=>X))
 
     measure_outcome=measure_syndrome!(reg, st)
@@ -43,7 +44,9 @@ end
     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, Yao.YaoBlocks.Optimise.to_basictypes(ps_ec_phy))
 
     @test measure_syndrome!(reg, st) == [1,1,1,1,1,1,1,1]