An MCMC decoding example

[nzy1997]

using TensorQEC
using Random

# code definition
tanner = CSSTannerGraph(SurfaceCode(3,3))
tanner = CSSTannerGraph(BivariateBicycleCode(6,12, ((3,0),(0,1),(0,2)), ((0,3),(1,0),(2,0))))

# error model and syndrome extraction
p=0.01
em = iid_error(p,p,p,TensorQEC.nq(tanner))
Random.seed!(1234)
error_qubits = random_error_qubits(em)
syd = syndrome_extraction(error_qubits, tanner)

# generate problem
prob,_ = TensorQEC.generate_spin_glass_sa(tanner, em, collect(0:1e-3:1.0), 100,false)
config = TensorQEC._mixed_integer_programming_for_one_solution(tanner, syd)

# get energy
TensorQEC.sa_energy_hard(fill(false, length(prob.ops)), prob, vcat(config[1],config[2]))

# get minimum energy with integer programming
res = decode(IPDecoder(),tanner,syd)
TensorQEC.sa_energy_hard(fill(false, length(prob.ops)), prob, vcat(res.error_qubits.xerror,res.error_qubits.zerror))

[Br0kenSmi1e]

I changed the first arg of energy function to all true vector and get following error

ERROR: BoundsError: attempt to access 18-element Vector{Mod2} at index [[-0.030459207484708574, -4.605170185988091, -4.605170185988091, -4.605170185988091]]
Stacktrace:
 [1] throw_boundserror(A::Vector{Mod2}, I::Tuple{SubArray{Float64, 1, Vector{Float64}, Tuple{UnitRange{Int64}}, true}})
   @ Base ./essentials.jl:14
 [2] checkbounds
   @ ./abstractarray.jl:699 [inlined]
 [3] view
   @ ./subarray.jl:214 [inlined]
 [4] maybeview
   @ ./views.jl:149 [inlined]
 [5] dotview
   @ ./broadcast.jl:1228 [inlined]
 [6] sa_energy_hard(ops_config::Vector{Bool}, sap::TensorQEC.SpinGlassSA{Float64, Int32}, bit_config::Vector{Mod2})
   @ TensorQEC ~/.julia/packages/TensorQEC/agxfv/src/decoding/mcmc.jl:189
 [7] top-level scope
   @ ~/jcode/qec/prob.jl:61

Code to reproduce this error:

using TensorQEC
using Random

tanner = CSSTannerGraph(SurfaceCode(3,3))

p = 0.01
em = iid_error(p,p,p,TensorQEC.nq(tanner))
Random.seed!(1234)
error_qubits = random_error_qubits(em)
syd = syndrome_extraction(error_qubits, tanner)

prob,_ = TensorQEC.generate_spin_glass_sa(tanner, em, collect(0:1e-3:1.0), 100,false)
config = TensorQEC._mixed_integer_programming_for_one_solution(tanner, syd)

# change the input vector false to true
TensorQEC.sa_energy_hard(fill(true, length(prob.ops)), prob, vcat(config[1],config[2]))

[nzy1997]

Thank you for pointing this out. This bug is fixed in 9a9225c.

[Br0kenSmi1e]

BB code produces error when constructing spin glass problem:

ERROR: MethodError: no method matching TensorQEC.SpinGlassSA(::TensorQEC.VecPtr{…}, ::TensorQEC.VecPtr{…}, ::TensorQEC.VecPtr{…}, ::TensorQEC.VecPtr{…}, ::TensorQEC.VecPtr{…}, ::Vector{…}, ::Int64, ::Vector{…})
The type `TensorQEC.SpinGlassSA` exists, but no method is defined for this combination of argument types when trying to construct it.

Closest candidates are:
  TensorQEC.SpinGlassSA(::TensorQEC.VecPtr{IT, IT}, ::TensorQEC.VecPtr{IT, IT}, ::TensorQEC.VecPtr{T, IT}, ::TensorQEC.VecPtr{IT, IT}, ::TensorQEC.VecPtr{IT, IT}, ::Vector{T}, ::Int64, ::Vector{Vector{Int64}}) where {T, IT}
   @ TensorQEC ~/.julia/packages/TensorQEC/k8JEN/src/decoding/mcmc.jl:9

Stacktrace:
 [1] generate_spin_glass_sa(tanner::CSSTannerGraph, ide::IndependentDepolarizingError{…}, betas::Vector{…}, num_trials::Int64, use_cuda::Bool; IT::Type{…})
   @ TensorQEC ~/.julia/packages/TensorQEC/k8JEN/src/decoding/mcmc.jl:36
 [2] generate_spin_glass_sa(tanner::CSSTannerGraph, ide::IndependentDepolarizingError{…}, betas::Vector{…}, num_trials::Int64, use_cuda::Bool)
   @ TensorQEC ~/.julia/packages/TensorQEC/k8JEN/src/decoding/mcmc.jl:19
 [3] top-level scope
   @ ~/jcode/qec/prob.jl:16
Some type information was truncated. Use `show(err)` to see complete types.

code to reproduce:

using TensorQEC
using Random

# code definition
tanner = CSSTannerGraph(BivariateBicycleCode(7, 7, ((3,0),(0,1),(0,2)), ((0,3),(1,0),(2,0))))

# error model and syndrome extraction
p = 0.01
em = iid_error(p,p,p,TensorQEC.nq(tanner))
Random.seed!(42)
error_qubits = random_error_pattern(em)
syd = syndrome_extraction(error_qubits, tanner)

# generate problem
prob,_ = TensorQEC.generate_spin_glass_sa(tanner, em, collect(0:1e-3:1.0), 100,false)