Introduce PastaQ.normalize! to distinguish from ITensors.normalize!

Project.toml

@@ -1,7 +1,7 @@
 name = "PastaQ"
 uuid = "30b07047-aa8b-4c78-a4e8-24d720215c19"
 authors = ["Giacomo Torlai <gttorlai@amazon.com>", "Matthew Fishman <mfishman@flatironinstitute.org>"]
-version = "0.0.19"
+version = "0.0.20"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

src/exports.jl

@@ -60,7 +60,6 @@ export
 
   # lpdo.jl
   LPDO,
-  normalize!,
   logtr,
   tr,
 

src/imports.jl

@@ -18,7 +18,6 @@ import ITensors:
   expect
 
 import LinearAlgebra: 
-  normalize!, 
   tr, 
   norm
 

test/array.jl

@@ -76,7 +76,7 @@ end
   end
 
   # LPDO density matrix
-  ρ = normalize!(randomstate(qubits; χ = 4, ξ = 2))
+  ρ = PastaQ.normalize!(randomstate(qubits; χ = 4, ξ = 2))
   ρmat = PastaQ.array(ρ)
 
   ρmpo = MPO(ρ)
@@ -125,7 +125,7 @@ end
 
   # LPDO Choi matrix
 
-  Λ = normalize!(randomprocess(qubits; χ = 4, ξ = 2); localnorm = 2)
+  Λ = PastaQ.normalize!(randomprocess(qubits; χ = 4, ξ = 2); localnorm = 2)
   Λmat = PastaQ.array(Λ)
 
   Λmpo = MPO(Λ)
@@ -181,7 +181,7 @@ end
   @test ρtest ≈ ρmat
 
   # LPDO density matrix
-  ρ = normalize!(randomstate(qubits; χ = 4, ξ = 2))
+  ρ = PastaQ.normalize!(randomstate(qubits; χ = 4, ξ = 2))
   ρmat = PastaQ.array(ρ)
   ρprod = prod(ρ)
   ρtest = PastaQ.array(ρprod)
@@ -200,7 +200,7 @@ end
   @test Λmat ≈ Λtest
 
   # LPDO Choi matrix
-  Λ = normalize!(randomprocess(qubits; χ = 4, ξ = 2); localnorm = 2)
+  Λ = PastaQ.normalize!(randomprocess(qubits; χ = 4, ξ = 2); localnorm = 2)
   Λmat = PastaQ.array(Λ)
   Λprod = prod(Λ)
   Λtest = PastaQ.array(Λprod)

test/distances.jl

@@ -17,8 +17,8 @@ using Random
   ρ1 = runcircuit(sites, circuit1; noise=("DEP", (p=0.01,)))
   ρ2 = runcircuit(sites, circuit2; noise=("DEP", (p=0.01,)))
   # LPDO density matrix
-  ϱ1 = normalize!(randomstate(sites; χ = 2, ξ = 3))
-  ϱ2 = normalize!(randomstate(sites; χ = 2, ξ = 3))
+  ϱ1 = PastaQ.normalize!(randomstate(sites; χ = 2, ξ = 3))
+  ϱ2 = PastaQ.normalize!(randomstate(sites; χ = 2, ξ = 3))
 
   ψ1vec = PastaQ.array(ψ1)
   ρ1mat = PastaQ.array(ρ1)
@@ -86,13 +86,13 @@ end
   ρ1 = MPO(ϱ1) 
   ρ2 = MPO(ϱ2)
 
-  ψ1vec = PastaQ.array(normalize!(copy(ψ1)))
-  ρ1mat = PastaQ.array(normalize!(copy(ρ1)))  
-  ϱ1mat = PastaQ.array(normalize!(copy(ϱ1)))
+  ψ1vec = PastaQ.array(PastaQ.normalize!(copy(ψ1)))
+  ρ1mat = PastaQ.array(PastaQ.normalize!(copy(ρ1)))  
+  ϱ1mat = PastaQ.array(PastaQ.normalize!(copy(ϱ1)))
 
-  ψ2vec = PastaQ.array(normalize!(copy(ψ2)))
-  ρ2mat = PastaQ.array(normalize!(copy(ρ2)))
-  ϱ2mat = PastaQ.array(normalize!(copy(ϱ2)))
+  ψ2vec = PastaQ.array(PastaQ.normalize!(copy(ψ2)))
+  ρ2mat = PastaQ.array(PastaQ.normalize!(copy(ρ2)))
+  ϱ2mat = PastaQ.array(PastaQ.normalize!(copy(ϱ2)))
 
   @test fidelity(ψ1, ψ2) ≈ abs2(ψ1vec' * ψ2vec)
   @test fidelity(ψ1, ρ2) ≈ ψ1vec' * ρ2mat * ψ1vec
@@ -151,20 +151,20 @@ end
   ρ1 = PastaQ.choimatrix(sites, circuit1; noise=("DEP", (p=0.01,)))
   ρ2 = PastaQ.choimatrix(sites, circuit2; noise=("DEP", (p=0.01,)))
   # LPDO Choi matrix
-  ϱ1 = normalize!(randomprocess(sites; mixed=true))
-  ϱ2 = normalize!(randomprocess(sites; mixed=true))
+  ϱ1 = PastaQ.normalize!(randomprocess(sites; mixed=true))
+  ϱ2 = PastaQ.normalize!(randomprocess(sites; mixed=true))
 
   @disable_warn_order begin
     ϕ1 = PastaQ.unitary_mpo_to_choi_mps(U1)
-    normalize!(ϕ1)
+    PastaQ.normalize!(ϕ1)
     ϕ1vec = PastaQ.array(ϕ1)
     ρ1mat = PastaQ.array(ρ1)
     ρ1mat = ρ1mat / tr(ρ1mat)
     ϱ1mat = PastaQ.array(ϱ1)
     ϱ1mat = ϱ1mat / tr(ϱ1mat)
 
     ϕ2 = PastaQ.unitary_mpo_to_choi_mps(U2)
-    normalize!(ϕ2)
+    PastaQ.normalize!(ϕ2)
     ϕ2vec = PastaQ.array(ϕ2)
     ρ2mat = PastaQ.array(ρ2)
     ρ2mat = ρ2mat / tr(ρ2mat)
@@ -221,13 +221,13 @@ end
 
   @disable_warn_order begin
     ϕ1 = PastaQ.unitary_mpo_to_choi_mps(U1)
-    normalize!(ϕ1)
+    PastaQ.normalize!(ϕ1)
     ϕ1vec = PastaQ.array(ϕ1)
     ϱ1mat = PastaQ.array(ϱ1)
     ϱ1mat = ϱ1mat / tr(ϱ1mat)
 
     ϕ2 = PastaQ.unitary_mpo_to_choi_mps(U2)
-    normalize!(ϕ2)
+    PastaQ.normalize!(ϕ2)
     ϕ2vec = PastaQ.array(ϕ2)
     ϱ2mat = PastaQ.array(ϱ2)
     ϱ2mat = ϱ2mat / tr(ϱ2mat)

test/optimizers.jl

@@ -102,7 +102,7 @@ end
 
   U = randomprocess(N; χ=χ)
   Φ = LPDO(PastaQ.unitary_mpo_to_choi_mps(U))
-  normalize!(Φ; localnorm=2)
+  PastaQ.normalize!(Φ; localnorm=2)
 
   opt = Optimisers.Descent(0.1) 
   st = PastaQ.state(opt, Φ)
@@ -128,7 +128,7 @@ end
   data = data_in .=> data_out
 
   Λ = randomprocess(N; χ=χ, ξ = 3)
-  normalize!(Λ; localnorm=2)
+  PastaQ.normalize!(Λ; localnorm=2)
 
   opt = Optimisers.Descent(0.1) 
   st = PastaQ.state(opt, Λ)

test/processtomography.jl

@@ -254,7 +254,7 @@ end
   @test length(Λ) == N
   logZ = 2 * lognorm(Λ.X)
   sqrt_localZ = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localZ, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localZ, localnorm=2)
   @test logZ ≈ N * log(2) + 2.0 * sum(log.(sqrt_localZ))
   @test abs2(norm(Λ.X)) ≈ 2^N
 end
@@ -269,7 +269,7 @@ end
   num_grad = numgradslogZ(Λ)
 
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
   @test norm(Λ.X)^2 ≈ 2^N
   alg_grad, _ = PastaQ.gradlogZ(Λ; sqrt_localnorms=sqrt_localnorms)
 
@@ -296,7 +296,7 @@ end
   Λ = LPDO(PastaQ.unitary_mpo_to_choi_mps(U))
   num_grad = numgradsnll(Λ, data)
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
 
   alg_grad, _ = PastaQ.gradnll(Λ, data; sqrt_localnorms=sqrt_localnorms)
   for j in 1:N
@@ -316,7 +316,7 @@ end
   num_grad = numgradsTP(Λ; accuracy=1e-8)
   Γ_test = PastaQ.TP(Λ)
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
 
   alg_grad_logZ, logZ = PastaQ.gradlogZ(Λ; sqrt_localnorms=sqrt_localnorms)
 
@@ -355,7 +355,7 @@ end
   num_grads = num_gradZ + num_gradNLL + trace_preserving_regularizer * num_gradTP
 
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
 
   ex_loss = PastaQ.nll(Λ, data) + 2 * lognorm(Λ.X)
   alg_grads, loss = PastaQ.gradients(
@@ -381,7 +381,7 @@ end
   @test length(Λ) == N
   logZ = logtr(Λ)
   localZ = []
-  normalize!(Λ; (sqrt_localnorms!)=localZ, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=localZ, localnorm=2)
   @test logZ ≈ N * log(2) + 2.0 * sum(log.(localZ))
   @test tr(Λ) ≈ 2^N
 end
@@ -394,7 +394,7 @@ end
   Λ = randomprocess(N; mixed=true, χ=χ, ξ=ξ)
   num_grad = numgradslogZ(Λ)
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
   @test tr(Λ) ≈ 2^N
   alg_grad, _ = PastaQ.gradlogZ(Λ; sqrt_localnorms=sqrt_localnorms)
 
@@ -421,7 +421,7 @@ end
   Λ = randomprocess(N; mixed=true, χ=χ, ξ=ξ)
   num_grad = numgradsnll(Λ, data)
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
   alg_grad, loss = PastaQ.gradnll(Λ, data; sqrt_localnorms=sqrt_localnorms)
   @test loss ≈ PastaQ.nll(Λ, data)
 
@@ -444,7 +444,7 @@ end
   num_grad = numgradsTP(Λ; accuracy=1e-8)
   Γ_test = PastaQ.TP(Λ)
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
   Γ_test = PastaQ.TP(Λ)
 
   alg_grad_logZ, logZ = PastaQ.gradlogZ(Λ; sqrt_localnorms=sqrt_localnorms)
@@ -477,7 +477,7 @@ end
   Λ = randomprocess(N; mixed=true, χ=χ, ξ=ξ)
   num_grad = numgradsnll(Λ, data)
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
   alg_grad, loss = PastaQ.gradnll(Λ, data; sqrt_localnorms=sqrt_localnorms)
 
   TP_distance = PastaQ.TP(Λ)
@@ -490,7 +490,7 @@ end
   num_grads = num_gradZ + num_gradNLL + trace_preserving_regularizer * num_gradTP
 
   sqrt_localnorms = []
-  normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
+  PastaQ.normalize!(Λ; (sqrt_localnorms!)=sqrt_localnorms, localnorm=2)
 
   ex_loss = PastaQ.nll(Λ, data) + 2 * lognorm(Λ.X)
   alg_grads, loss = PastaQ.gradients(

test/statetomography.jl

@@ -177,7 +177,7 @@ numgradsnll(M::MPS, args...; kwargs...) = numgradsnll(LPDO(M), args...; kwargs..
   @test length(ψ) == N
   logZ = lognorm(ψ)
   localZ = []
-  normalize!(ψ; (localnorms!)=localZ)
+  PastaQ.normalize!(ψ; (localnorms!)=localZ)
   @test logZ ≈ sum(log.(localZ))
   @test norm(ψ) ≈ 1
 end
@@ -196,7 +196,7 @@ end
 
   # 2. Globally normalized
   ψ = randomstate(N; χ=χ)
-  normalize!(ψ)
+  PastaQ.normalize!(ψ)
   @test norm(ψ)^2 ≈ 1
   alg_grad, _ = PastaQ.gradlogZ(ψ)
   num_grad = numgradslogZ(ψ)
@@ -209,7 +209,7 @@ end
   num_grad = numgradslogZ(ψ)
 
   localnorms = []
-  normalize!(ψ; (localnorms!)=localnorms)
+  PastaQ.normalize!(ψ; (localnorms!)=localnorms)
   @test norm(ψ) ≈ 1
   alg_grad, _ = PastaQ.gradlogZ(ψ; localnorms=localnorms)
   for j in 1:N
@@ -234,7 +234,7 @@ end
 
   # 2. Globally normalized
   ψ = randomstate(N; χ=χ)
-  normalize!(ψ)
+  PastaQ.normalize!(ψ)
   num_grad = numgradsnll(ψ, data)
   alg_grad, loss = PastaQ.gradnll(ψ, data)
   for j in 1:N
@@ -245,7 +245,7 @@ end
   ψ = randomstate(N; χ=χ)
   num_grad = numgradsnll(ψ, data)
   localnorms = []
-  normalize!(ψ; (localnorms!)=localnorms)
+  PastaQ.normalize!(ψ; (localnorms!)=localnorms)
   @test norm(ψ) ≈ 1
   alg_grad_localnorm, loss = PastaQ.gradnll(ψ, data; localnorms=localnorms)
   for j in 1:N
@@ -276,7 +276,7 @@ end
 
   # 2. Globally normalized
   ψ = randomstate(N; χ=χ)
-  normalize!(ψ)
+  PastaQ.normalize!(ψ)
   num_gradZ = numgradslogZ(ψ)
   num_gradNLL = numgradsnll(ψ, data)
   num_grads = num_gradZ + num_gradNLL
@@ -297,7 +297,7 @@ end
   num_grads = num_gradZ + num_gradNLL
 
   localnorms = []
-  normalize!(ψ; (localnorms!)=localnorms)
+  PastaQ.normalize!(ψ; (localnorms!)=localnorms)
   NLL = PastaQ.nll(ψ, data)
   ex_loss = NLL
   @test norm(ψ)^2 ≈ 1
@@ -319,7 +319,7 @@ end
   @test length(ρ) == N
   logZ = logtr(ρ)
   localZ = []
-  normalize!(ρ; (sqrt_localnorms!)=localZ)
+  PastaQ.normalize!(ρ; (sqrt_localnorms!)=localZ)
   @test logZ ≈ 2.0 * sum(log.(localZ))
   @test tr(ρ) ≈ 1
 end
@@ -345,7 +345,7 @@ end
 
   # 2. Globally normalized
   ρ = randomstate(N; mixed=true, χ=χ, ξ=ξ)
-  normalize!(ρ)
+  PastaQ.normalize!(ρ)
   @test tr(ρ) ≈ 1
   alg_grad, _ = PastaQ.gradlogZ(ρ)
   num_grad = numgradslogZ(ρ)
@@ -364,7 +364,7 @@ end
   num_grad = numgradslogZ(ρ)
 
   sqrt_localnorms = []
-  normalize!(ρ; (sqrt_localnorms!)=sqrt_localnorms)
+  PastaQ.normalize!(ρ; (sqrt_localnorms!)=sqrt_localnorms)
   @test tr(ρ) ≈ 1
   alg_grad, _ = PastaQ.gradlogZ(ρ; sqrt_localnorms=sqrt_localnorms)
 
@@ -404,7 +404,7 @@ end
 
   ## 2. Globally normalized
   ρ = randomstate(N; mixed=true, χ=χ, ξ=ξ)
-  normalize!(ρ)
+  PastaQ.normalize!(ρ)
   num_grad = numgradsnll(ρ, data)
   alg_grad, loss = PastaQ.gradnll(ρ, data)
 
@@ -421,7 +421,7 @@ end
   ρ = randomstate(N; mixed=true, χ=χ, ξ=ξ)
   num_grad = numgradsnll(ρ, data)
   sqrt_localnorms = []
-  normalize!(ρ; (sqrt_localnorms!)=sqrt_localnorms)
+  PastaQ.normalize!(ρ; (sqrt_localnorms!)=sqrt_localnorms)
   @test tr(ρ) ≈ 1
   alg_grad, loss = PastaQ.gradnll(ρ, data; sqrt_localnorms=sqrt_localnorms)
   alg_gradient = permutedims(ITensors.array(alg_grad[1]), [3, 1, 2])