Fixed fulltomography

.gitignore

@@ -4,3 +4,4 @@ examples/data/
 test/data/
 data/
 Manifest.toml
+.vscode

Project.toml

@@ -5,16 +5,19 @@ version = "0.1.0"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+Convex = "f65535da-76fb-5f13-bab9-19810c17039a"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 HDF5 = "f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"
 ITensors = "9136182c-28ba-11e9-034c-db9fb085ebd5"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+MathOptInterface = "b8f27783-ece8-5eb3-8dc8-9495eed66fee"
 Observers = "338f10d5-c7f1-4033-a7d1-f9dec39bcaa0"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
+SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
 [compat]

README.md

@@ -15,7 +15,7 @@ PastaQ.jl is a Julia software toolbox providing a range of computational methods
 
 ---
 ## Install
-The PastaQ package can be installed with the Julia package manager. From the Julia REPL, type ] to enter the Pkg REPL mode and run:
+The PastaQ package can be installed with the Julia package manager. From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
 
 ```julia
 julia> ]

src/PastaQ.jl

@@ -27,20 +27,23 @@ include("optimizers.jl")
 include("productstates.jl")
 include("randomstates.jl")
 include("measurements.jl")
+# included the file and added SCS and Convex to the manifest. Module importing `SCS.MathOptInterface` also does not work, and it needs to be installed and imported separately. This has also been implemented.
+# fixed relevant tests as well.
+include("tomography/fulltomography.jl")
 include("tomography/tensornetwork-statetomography.jl")
 include("tomography/tensornetwork-processtomography.jl")
 include("tomography/quantumtomography.jl")
 include("io.jl")
 include("array.jl")
 include("utils.jl")
 
-using Requires
-function __init__()
-  @require SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13" begin
-    @require Convex = "f65535da-76fb-5f13-bab9-19810c17039a" include(
-      "tomography/fulltomography.jl"
-    )
-  end
-end
+# using Requires
+# function __init__()
+#   @require SCS = "c946c3f1-0d1f-5ce8-9dea-7daa1f7e2d13" begin
+#     @require Convex = "f65535da-76fb-5f13-bab9-19810c17039a" include(
+#       "tomography/fulltomography.jl"
+#     )
+#   end
+# end
 
 end # module

src/tomography/fulltomography.jl

@@ -1,4 +1,10 @@
-const MOI = SCS.MathOptInterface
+using Convex
+using SCS
+import MathOptInterface
+const MOI = MathOptInterface
+
+# Added a specific import statement for Convex, SCS and MathOptInterface. 
+# The earlier implementation ran into a ModuleError here.
 
 function tomography(
   probabilities::Dict{Tuple,<:Dict},
@@ -34,14 +40,16 @@ function tomography(
     if (method == "LS" || method == "least_squares")
       # Minimize the cost function C = ||A ρ⃗ - p̂||²
       cost_function = Convex.norm(A * vec(ρ) - p)
-    elseif (method == "MLE" || method == "maximum_likelihood")
+    elseif (method == "ML" || method == "maximum_likelihood") 
       # Minimize the negative log likelihood:
+      # Ensured uniformity in documentation and implementation of the maximum_likelihood case. 
+      # the keyword was implemented as "MLS" in some places, "MLE" in some places. Now everything is consistent and two-lettered.
       cost_function = -p' * Convex.log(real(A * vec(ρ)) + 1e-10)
     else
       error("Tomography method not recognized
        Currently available methods: - LI  : linear inversion
                                       LS  : least squares
-                                      MLS : maximum likelihood")
+                                      ML : maximum likelihood")
     end
 
     # Contrained the trace and enforce positivity and hermitianity

test/runtests.jl

@@ -7,32 +7,43 @@ using Test
 # However this seems to cause some issues with some process
 # tomography tests, it seems to be sensitive to the ordering
 # they are called.
-filenames = [
-  "test_array.jl",
-  "test_autodiff.jl",
-  "test_circuits.jl",
-  "test_distances.jl",
-  "test_fulltomography.jl",
-  "test_gates.jl",
-  "test_getsamples.jl",
-  "test_io.jl",
-  "test_noise.jl",
-  "test_optimizers.jl",
-  "test_processtomography.jl",
-  "test_productstates.jl",
-  "test_qubitarrays.jl",
-  "test_randomstates.jl",
-  "test_runcircuit.jl",
-  "test_statetomography.jl",
-  "test_utils.jl",
-  "test_gpu.jl",
-]
+# filenames = [
+#   "test_array.jl",
+#   "test_autodiff.jl",
+#   "test_circuits.jl",
+#   "test_distances.jl",
+#   "test_fulltomography.jl",
+#   "test_gates.jl",
+#   "test_getsamples.jl",
+#   "test_io.jl",
+#   "test_noise.jl",
+#   "test_optimizers.jl",
+#   "test_processtomography.jl",
+#   "test_productstates.jl",
+#   "test_qubitarrays.jl",
+#   "test_randomstates.jl",
+#   "test_runcircuit.jl",
+#   "test_statetomography.jl",
+#   "test_utils.jl",
+#   "test_gpu.jl",
+# ]
+
+# Implement a single function to read all files with a filter  
+# all tests now pass, at least with a single thread.
+# Also, removed the condition to check if filename starts with "test_" as it is guaranteed by the filter used in searchDir().
+# Can upgrade filter to broadcast
+
+searchDir(path, key) = filter(x->(all([ occursin(k,x) for k in key ]) ), readdir(path))
+filenames = searchDir("./",["test_",".jl"])
 
 @testset "PastaQ.jl" begin
   @testset "$filename" for filename in filenames
-    if startswith(filename, "test_") && endswith(filename, ".jl")
+    # if endswith(filename, ".jl") #&& startswith(filename, "test_") 
       println("Running $filename")
       @time include(filename)
-    end
+      # end 
   end
+
+    #@time include("test_io.jl")
+
 end

test/test_fulltomography.jl

@@ -91,7 +91,7 @@ end
   λ = first(eigen(ρ))
   @test all(real(λ) .≥ -1e-3)
 
-  ρ = PastaQ.array(tomography(samples; method="MLE"))
+  ρ = PastaQ.array(tomography(samples; method="ML")) # Ensured method keyword consistency
   λ = first(eigen(ρ))
   @test all(real(λ) .≥ -1e-2)
 end
@@ -111,7 +111,7 @@ end
   @test tr(ρ) ≈ 2.0
   ρ = PastaQ.array(tomography(samples; method="LS", trρ=2.0))
   @test tr(ρ) ≈ 2.0 atol = 1e-4
-  ρ = PastaQ.array(tomography(samples; method="MLE", trρ=2.0))
+  ρ = PastaQ.array(tomography(samples; method="ML", trρ=2.0)) # Ensured method keyword consistency
   @test tr(ρ) ≈ 2.0 atol = 1e-4
 end
 

test/test_gpu.jl

@@ -11,12 +11,14 @@ const devices = (
 
 const full_representations = (false, true)
 
-const noises = (nothing, ("amplitude_damping", (γ=0.1,)))
+const noises = (nothing, 
+                ("amplitude_damping", (γ=0.1,)),
+                ("phase_damping", (γ=0.1,))
+              )
 
 const processes = (false, true)
 
-@testset "runcircuit with eltype $eltype, device $device, full_representation $full_representation, noise $noise" for eltype in
-                                                                                                                      eltypes,
+@testset "runcircuit with eltype $eltype, device $device, full_representation $full_representation, noise $noise" for eltype in eltypes,
   device in devices,
   full_representation in full_representations,
   noise in noises,
@@ -29,3 +31,18 @@ const processes = (false, true)
     @test Base.eltype(ψ[1]) === eltype
   end
 end
+
+# Added a new test set for depolarizing noise. Since the
+
+@testset "runcircuit with eltype $eltype, device $device, full_representation $full_representation, noise depolarizing" for eltype in (nothing, ComplexF32, ComplexF64),
+  device in devices,
+  full_representation in full_representations,
+  process in processes
+
+  ψ = runcircuit([("X", 1)]; eltype, device, full_representation, noise=("depolarizing",(p=0.1,)), process)
+  if isnothing(eltype)
+    @test Base.eltype(ψ[1]) === ComplexF64
+  else
+    @test Base.eltype(ψ[1]) === eltype
+  end
+end

test/test_io.jl

@@ -8,12 +8,18 @@ using JLD2
 using HDF5
 using Optimisers
 
+# create a data/ directory to store results of testing, so they are not accidentally included in the git. 
+# This repo is already excluded in the gitignore
+
+Base.mkpath("data/")
+prefix = "data"
+
 @testset "write and read samples" begin
   N = 4
   depth = 2
   nshots = 100
   circuit = randomcircuit(N; depth=depth, twoqubitgates="CX", onequbitgates="Rx")
-  path = "test_data_writesamples.h5"
+  path = prefix*"/"*"test_data_writesamples.h5"
 
   X = runcircuit(circuit)
   data = getsamples(X, nshots)
@@ -107,8 +113,7 @@ end
   circuit = randomcircuit(N; depth=depth)
   layer = Tuple[]
   sites = siteinds("Qubit", N)
-
-  outputpath = "simulation"
+  outputpath = prefix*"/"*"simulation"
   ϕ = randomstate(sites; χ=10, normalize=true)
   ψ = runcircuit(sites, circuit)
   Ftest = fidelity(ψ, ϕ)
@@ -132,7 +137,7 @@ end
   ##   @test last(v) ≈ obs[!, k]
   ## end
 
-  fin = h5open("simulation_state.h5", "r")
+  fin = h5open(prefix*"/"*"simulation_state.h5", "r")
   M = read(fin, "state", MPS)
   close(fin)
   @test M ≈ ψ
@@ -152,7 +157,7 @@ end
     Ftest = fidelity(ϱ, ρ)
     g(ρ::MPO; kwargs...) = fidelity(ρ, ϱ)#; kwargs...) = fidelity(ψ, ϕ)
     obs = observer(["g" => g])
-    outputpath = "simulation"
+    outputpath = prefix*"/"*"simulation"
     ρ₀ = projector(productstate(sites))
     ρ = runcircuit(
       ρ₀,
@@ -174,7 +179,7 @@ end
   ##   @test last(v) ≈ obs[!, k]
   ## end
 
-  fin = h5open("simulation_state.h5", "r")
+  fin = h5open(prefix*"/"*"simulation_state.h5", "r")
   M = read(fin, "state", MPO)
   close(fin)
   @test M ≈ ρ
@@ -203,7 +208,7 @@ end
   batchsize = 10
   observe_step = 3
 
-  outputpath = "simulation"
+  outputpath = prefix*"/"*"simulation"
   ψ = tomography(
     data,
     ψ0;
@@ -224,7 +229,7 @@ end
   ##   @test last(v) ≈ obs[!, k]
   ## end
 
-  fin = h5open("simulation_state.h5", "r")
+  fin = h5open(prefix*"/"*"simulation_state.h5", "r")
   M = read(fin, "state", MPS)
   close(fin)
   @test M ≈ ψ
@@ -253,7 +258,7 @@ end
   batchsize = 10
   observe_step = 3
 
-  outputpath = "simulation"
+  outputpath = prefix*"/"*"simulation"
   ρ = tomography(
     data,
     ρ;
@@ -274,7 +279,7 @@ end
   ##   @test last(v) ≈ obs[!, k]
   ## end
 
-  fin = h5open("simulation_state.h5", "r")
+  fin = h5open(prefix*"/"*"simulation_state.h5", "r")
   M = read(fin, "state", LPDO{MPO})
   close(fin)
   @test M.X ≈ ρ.X
@@ -303,7 +308,7 @@ end
   batchsize = 10
   observe_step = 3
 
-  outputpath = "simulation"
+  outputpath = prefix*"/"*"simulation"
   U = tomography(
     data,
     U0;
@@ -325,7 +330,7 @@ end
   ##   @test last(v) ≈ obs[!, k]
   ## end
 
-  fin = h5open("simulation_state.h5", "r")
+  fin = h5open(prefix*"/"*"simulation_state.h5", "r")
   M = read(fin, "state", MPO)
   close(fin)
   @test M ≈ U
@@ -355,7 +360,7 @@ end
   batchsize = 10
   observe_step = 3
 
-  outputpath = "simulation"
+  outputpath = prefix*"/"*"simulation"
   Λ = tomography(
     data,
     Λ;
@@ -376,7 +381,7 @@ end
   ##   @test last(v) ≈ obs[!, k]
   ## end
 
-  fin = h5open("simulation_state.h5", "r")
+  fin = h5open(prefix*"/"*"simulation_state.h5", "r")
   M = read(fin, "state", LPDO{MPO})
   close(fin)
   @test M.X ≈ Λ.X