Gaugefields

Abstract

[NOTE: This is an extended version by Okuto Morikawa, in order to implement higher-form gauge fields (i.e., 't Hooft twisted boundary condition/flux).]

[NOTE: O.M. also provides a memory-safer code set than the original one.]

This is a package for lattice QCD codes. Treating gauge fields (links), gauge actions with MPI and autograd.

This package will be used in LatticeQCD.jl. See also the orginal package in Gaugefields.jl.

O.M. would like to thank Yuki Nagai and Akio Tomiya (the contributors of the original package). O.M. is also grateful to Hiroshi Suzuki.

What this package can do:

This package has following functionarities

SU(Nc) (Nc > 1) gauge fields in 2 or 4 dimensions with arbitrary actions.
Z(Nc) 2-form gauge fields in 4 dimensions, which are given as 't Hooft flux.
U(1) gauge fields in 2 dimensions with arbitrary actions.
Configuration generation
- Heatbath
- quenched Hybrid Monte Carlo
- quenched Hybrid Monte Carlo being subject to 't Hooft twisted b.c.
  - with external (non-dynamical) Z(Nc) 2-form gauge fields
- quenched Hybrid Monte Carlo for SU(Nc)/Z(Nc) gauge theory
  - with dynamical Z(Nc) 2-form gauge fields
Gradient flow via RK3
- Yang-Mills gradient flow
- Yang-Mills gradient flow being subject to 't Hooft twisted b.c.
- Gradient flow for SU(Nc)/Z(Nc) gauge theory
I/O: ILDG and Bridge++ formats are supported (c-lime will be installed implicitly with CLIME_jll )
MPI parallel computation (experimental. See documents.)
- quenched HMC with MPI being subject to 't Hooft twisted b.c.

The implementation of higher-form gauge fields is based on arXiv:2303.10977 [hep-lat].

Dynamical fermions will be supported with LatticeDiracOperators.jl.

In addition, this supports followings

Autograd for functions with SU(Nc) variables
Stout smearing (exp projecting smearing)
Stout force via backpropagation

Autograd can be worked for general Wilson lines except for ones have overlaps.

Install

add Wilsonloop
add https://github.com/o-morikawa/Gaugefields.jl.git

Development mode

This is a non-official package in Julia, and you are recommended to use it as a develop (dev) package if there's a possibility that you use the original Gaugefields.jl package or modify it.

To install the oringinal package, in Julia REPL in the package mode,

add Gaugefields.jl

Download the code locally, then in Julia REPL in the package mode,

dev /<your full path>/Gaugefields

When you use this package in Julia REPL, in the package mode,

activate Gaugefields

or, when in command line,

julia --project="Gaugefields" test.jl

How to use

Please see the orginal docs in Gaugefields.jl. Basically, you can use this package in a same way as the original code if the argument of any function, (..., U, ...), is rewritten by (..., U, B, ...).

Generating configurations and File loading

ILDG format for SU(N) guage fields

ILDG format is one of standard formats for LatticeQCD configurations.

We can read ILDG format like:

NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
Dim = 4

U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")

ildg = ILDG(filename)
i = 1
L = [NX,NY,NZ,NT]
load_gaugefield!(U,i,ildg,L,NC)

Then, we can calculate the plaquette:

temps = Temporalfields(U[1], num=2)
comb, factor = set_comb(U,Dim)

@time plaq_t = calculate_Plaquette(U,B,temps)*factor
println("plaq_t = $plaq_t")
poly = calculate_Polyakov_loop(U,temps) 
println("polyakov loop = $(real(poly)) $(imag(poly))")

We can write a configuration as the ILDG format like

filename = "hoge.ildg"
save_binarydata(U,filename)

Text format for Bridge++

Gaugefields.jl also supports a text format for Bridge++.

File loading

using Gaugefields

filename = "testconf.txt"
load_BridgeText!(filename,U,L,NC)

File saving

filename = "testconf.txt"
save_textdata(U,filename)

Z(N) 2-form gauge fields

SU(N) gauge fields possess Z(N) center symmetry, which is called 1-form global symmetry, a type of generalized symmetry. To gauge the 1-form center symmetry, we can define the Z(N) 2-form gauge fields in four dimensions, B, as

NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 0
NC = 3

flux=[1,0,0,0,0,1] # FLUX=[Z12,Z13,Z14,Z23,Z24,Z34]

println("Flux is ", flux)

U1 = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
B1 = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NY,condition = "tflux")

println("Initial conf of B at [1,2][2,2,:,:,NZ,NT]")
display(B1[1,2][2,2,:,:,NZ,NT])

Hybrid Monte Carlo

Non-dynamical higher-form gauge fields

We can do the HMC simulations. The example code is as follows.

using Random
using Gaugefields
using LinearAlgebra

function HMC_test_4D_tHooft(NX,NY,NZ,NT,NC,Flux,β)
    Dim = 4
    Nwing = 0

    flux = Flux
    println("Flux : ", flux)

    Random.seed!(123)


    U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold",randomnumber="Reproducible")
    B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NT,condition = "tflux")

    temps = Temporalfields(U[1], num=9)
    comb, factor = set_comb(U, Dim)

    @time plaq_t = calculate_Plaquette(U,B,temps)*factor
    println("0 plaq_t = $plaq_t")
    poly = calculate_Polyakov_loop(U,temps) 
    println("0 polyakov loop = $(real(poly)) $(imag(poly))")

    gauge_action = GaugeAction(U,B)
    plaqloop = make_loops_fromname("plaquette")
    append!(plaqloop,plaqloop')
    β = β/2
    push!(gauge_action,β,plaqloop)
    
    #show(gauge_action)

    p = initialize_TA_Gaugefields(U)
    Uold = similar(U)
    MDsteps = 50
    numaccepted = 0

    numtrj = 100
    for itrj = 1:numtrj
        t = @timed begin
            accepted = MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold,temps)
        end
        if get_myrank(U) == 0
#            println("elapsed time for MDsteps: $(t.time) [s]")
        end
        numaccepted += ifelse(accepted,1,0)

        #plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
        #println("$itrj plaq_t = $plaq_t")
        
        if itrj % 10 == 0
            @time plaq_t = calculate_Plaquette(U,B,temps)*factor
            println("$itrj plaq_t = $plaq_t")
            poly = calculate_Polyakov_loop(U,temps) 
            println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
            println("acceptance ratio ",numaccepted/itrj)
        end

    end
    return plaq_t,numaccepted/numtrj

end


function main()
    β = 5.7
    NX = 4
    NY = 4
    NZ = 4
    NT = 4
    NC = 3
    Flux = [0,0,1,1,0,0]
    #HMC_test_4D(NX,NY,NZ,NT,NC,β)
    HMC_test_4D_tHooft(NX,NY,NZ,NT,NC,Flux,β)
end
main()

Dynamical higher-form gauge fields

HMC simulations with dynamical B fields are as follows:

using Random
using Gaugefields
using Wilsonloop
using LinearAlgebra

function HMC_test_4D_dynamicalB(NX,NY,NZ,NT,NC,β)
    Dim = 4
    Nwing = 0

    Random.seed!(123)

    flux = [1,1,1,1,2,0]

    U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold",randomnumber="Reproducible")
    B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NT,condition = "tflux")

    L = [NX,NY,NZ,NT]
    filename = "test/confs/U_beta6.0_L8_F111120_4000.txt"
    load_BridgeText!(filename,U,L,NC)

    temps = Temporalfields(U[1], num=9)
    comb, factor = set_comb(U, Dim)

    @time plaq_t = calculate_Plaquette(U,B,temps)*factor
    println("0 plaq_t = $plaq_t")
    poly = calculate_Polyakov_loop(U,temps) 
    println("0 polyakov loop = $(real(poly)) $(imag(poly))")

    gauge_action = GaugeAction(U,B)
    plaqloop = make_loops_fromname("plaquette")
    append!(plaqloop,plaqloop')
    β = β/2
    push!(gauge_action,β,plaqloop)
    
    #show(gauge_action)

    p = initialize_TA_Gaugefields(U)
    Uold  = similar(U)
    Bold = similar(B)
    flux_old = zeros(Int, 6)

    MDsteps = 50 # even integer!!!
    numaccepted = 0

    numtrj = 100
    for itrj = 1:numtrj
        t = @timed begin
            accepted = MDstep_dynB!(
                gauge_action,
                U,
                B,
                flux,
                p,
                MDsteps,
                Dim,
                Uold,
                Bold,
                flux_old,
                temps
            )
        end
        if get_myrank(U) == 0
             println("Flux : ", flux)
#            println("elapsed time for MDsteps: $(t.time) [s]")
        end
        numaccepted += ifelse(accepted,1,0)

        #plaq_t = calculate_Plaquette(U,B,temps)*factor
        #println("$itrj plaq_t = $plaq_t")
        
        if itrj % 10 == 0
            @time plaq_t = calculate_Plaquette(U,B,temps)*factor
            println("$itrj plaq_t = $plaq_t")
            poly = calculate_Polyakov_loop(U,temps)
            println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
            println("acceptance ratio ",numaccepted/itrj)
        end

    end
    return plaq_t,numaccepted/numtrj

end


function main()
    β = 6.0
    NX = 8
    NY = 8
    NZ = 8
    NT = 8
    NC = 3
    HMC_test_4D_dynamicalB(NX,NY,NZ,NT,NC,β)
end
main()

Gradient flow

A simple case

We can use Lüscher's gradient flow.

NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 0
NC = 3

flux=[1,0,0,0,0,1] # FLUX=[Z12,Z13,Z14,Z23,Z24,Z34]

U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NY,condition = "tflux")


temps = Temporalfields(U[1], num=3)
comb, factor = set_comb(U,Dim)

g = Gradientflow(U, B)
for itrj=1:100
    flow!(U,B,g)
    @time plaq_t = calculate_Plaquette(U,B,temps)*factor
    println("$itrj plaq_t = $plaq_t")
    poly = calculate_Polyakov_loop(U,temps) 
    println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
end

Gradient flow with general terms

We can do the gradient flow with general terms with the use of Wilsonloop.jl, which is shown below. The coefficient of the action can be complex. The complex conjugate of the action defined here is added automatically to make the total action hermitian.
The code is

using Random
using Test
using Gaugefields
using Wilsonloop

function gradientflow_test_4D(NX,NY,NZ,NT,NC)
    Dim = 4
    Nwing = 1

    Random.seed!(123)

    U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot",randomnumber="Reproducible")

    temps = Temporalfields(U[1], num=2)
    comb, factor = set_comb(U, Dim)

    @time plaq_t = calculate_Plaquette(U,temps)*factor
    println("0 plaq_t = $plaq_t")
    poly = calculate_Polyakov_loop(U,temps) 
    println("0 polyakov loop = $(real(poly)) $(imag(poly))")

    #Plaquette term
    loops_p = Wilsonline{Dim}[]
    for μ=1:Dim
        for ν=μ:Dim
            if ν == μ
                continue
            end
            loop1 = Wilsonline([(μ,1),(ν,1),(μ,-1),(ν,-1)],Dim = Dim)
            push!(loops_p,loop1)
        end
    end

    #Rectangular term
    loops = Wilsonline{Dim}[]
    for μ=1:Dim
        for ν=μ:Dim
            if ν == μ
                continue
            end
            loop1 = Wilsonline([(μ,1),(ν,2),(μ,-1),(ν,-2)],Dim = Dim)
            push!(loops,loop1)
            loop1 = Wilsonline([(μ,2),(ν,1),(μ,-2),(ν,-1)],Dim = Dim)
            
            push!(loops,loop1)
        end
    end

    listloops = [loops_p,loops]
    listvalues = [1+im,0.1]
    g = Gradientflow_general(U,listloops,listvalues,eps = 0.01)

    for itrj=1:100
        flow!(U,g)
        if itrj % 10 == 0
            @time plaq_t = calculate_Plaquette(U,temps)*factor
            println("$itrj plaq_t = $plaq_t")
            poly = calculate_Polyakov_loop(U,temps) 
            println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
        end
    end
    return plaq_t

end


function gradientflow_test_2D(NX,NT,NC)
    Dim = 2
    Nwing = 1
    U = Initialize_Gaugefields(NC,Nwing,NX,NT,condition = "hot",randomnumber="Reproducible")

    temps = Temporalfields(U[1], num=2)
    comb, factor = set_comb(U, Dim)

    @time plaq_t = calculate_Plaquette(U,temps)*factor
    println("0 plaq_t = $plaq_t")
    poly = calculate_Polyakov_loop(U,temps) 
    println("0 polyakov loop = $(real(poly)) $(imag(poly))")

    #g = Gradientflow(U,eps = 0.01)
    #listnames = ["plaquette"]
    #listvalues = [1]
    loops_p = Wilsonline{Dim}[]
    for μ=1:Dim
        for ν=μ:Dim
            if ν == μ
                continue
            end

            loop1 = Wilsonline([(μ,1),(ν,1),(μ,-1),(ν,-1)],Dim = Dim)
            push!(loops_p,loop1)

        end
    end


    loops = Wilsonline{Dim}[]
    for μ=1:Dim
        for ν=μ:Dim
            if ν == μ
                continue
            end
            loop1 = Wilsonline([(μ,1),(ν,2),(μ,-1),(ν,-2)],Dim = Dim)
            push!(loops,loop1)
            loop1 = Wilsonline([(μ,2),(ν,1),(μ,-2),(ν,-1)],Dim = Dim)
            
            push!(loops,loop1)
        end
    end

    listloops = [loops_p,loops]
    listvalues = [1+im,0.1]
    g = Gradientflow_general(U,listloops,listvalues,eps = 0.01)

    for itrj=1:100
        flow!(U,g)
        if itrj % 10 == 0
            @time plaq_t = calculate_Plaquette(U,temps)*factor
            println("$itrj plaq_t = $plaq_t")
            poly = calculate_Polyakov_loop(U,temps)
            println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
        end
    end

    return plaq_t

end



const eps = 0.1


println("2D system")
@testset "2D" begin
    NX = 4
    #NY = 4
    #NZ = 4
    NT = 4
    Nwing = 1

    @testset "NC=1" begin
        β = 2.3
        NC = 1
        println("NC = $NC")
        @time plaq_t = gradientflow_test_2D(NX,NT,NC)
    end
    #error("d")
    
    @testset "NC=2" begin
        β = 2.3
        NC = 2
        println("NC = $NC")
        @time plaq_t = gradientflow_test_2D(NX,NT,NC)
    end

    @testset "NC=3" begin
        β = 5.7
        NC = 3
        println("NC = $NC")
        @time plaq_t = gradientflow_test_2D(NX,NT,NC)
    end

    @testset "NC=4" begin
        β = 5.7
        NC = 4
        println("NC = $NC")
        @time plaq_t = gradientflow_test_2D(NX,NT,NC)
    end
end

println("4D system")
@testset "4D" begin
    NX = 4
    NY = 4
    NZ = 4
    NT = 4
    Nwing = 1


    
    @testset "NC=2" begin
        β = 2.3
        NC = 2
        println("NC = $NC")
        @time plaq_t = gradientflow_test_4D(NX,NY,NZ,NT,NC)
    end

    @testset "NC=3" begin
        β = 5.7
        NC = 3
        println("NC = $NC")
        @time plaq_t = gradientflow_test_4D(NX,NY,NZ,NT,NC)
    end

    @testset "NC=4" begin
        β = 5.7
        NC = 4
        println("NC = $NC")

        val = 0.7301232810349298
        @time plaq_t =gradientflow_test_4D(NX,NY,NZ,NT,NC)
    end


end

HMC with MPI

Here, we show the HMC with MPI. the REPL and Jupyternotebook can not be used when one wants to use MPI. At first, in Julia REPL in the package mode,

add MPI

Then,

using MPI
MPI.install_mpiexecjl()

and

export PATH="/<your home path>/.julia/bin/:$PATH"

The command is like:

mpiexecjl --project="Gaugefields" -np 2 julia mpi_sample.jl 1 1 1 2 true

1 1 1 2 means PEX PEY PEZ PET. In this case, the time-direction is diveded by 2.

The sample code is written as

using Random
using Gaugefields
using Wilsonloop
using LinearAlgebra
using MPI

if length(ARGS) < 5
    error("USAGE: ","""
    mpirun -np 2 exe.jl 1 1 1 2 true
    """)
end
const pes = Tuple(parse.(Int64,ARGS[1:4]))
const mpi = parse(Bool,ARGS[5])

function HMC_test_4D_tHooft(NX,NY,NZ,NT,NC,Flux,β)
    Dim = 4
    Nwing = 0

    flux = Flux

    Random.seed!(123)

    if mpi
        PEs = pes#(1,1,1,2)
        U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot",mpi=true,PEs = PEs,mpiinit = false) 
        B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NT,condition = "tflux",mpi=true,PEs = PEs,mpiinit = false)
    else
        U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "hot")
        B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NT,condition = "tflux")
    end

    if get_myrank(U) == 0
        println("Flux : ", flux)
    end

    if get_myrank(U) == 0
        println(typeof(U))
    end


    temps = Temporalfields(U[1], num=10)
    comb, factor = set_comb(U, Dim)

    @time plaq_t = calculate_Plaquette(U,B,temps)*factor
    if get_myrank(U) == 0
        println("0 plaq_t = $plaq_t")
    end
    poly = calculate_Polyakov_loop(U,temps) 
    if get_myrank(U) == 0
        println("0 polyakov loop = $(real(poly)) $(imag(poly))")
    end

    gauge_action = GaugeAction(U,B)
    plaqloop = make_loops_fromname("plaquette")
    append!(plaqloop,plaqloop')
    β = β/2
    push!(gauge_action,β,plaqloop)
    
    #show(gauge_action)

    p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients. 
    Uold = similar(U)
    MDsteps = 50
    numaccepted = 0

    numtrj = 100
    for itrj = 1:numtrj
        t = @timed begin
            accepted = MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold,temps)
        end
        if get_myrank(U) == 0
            println("elapsed time for MDsteps: $(t.time) [s]")
        end
        numaccepted += ifelse(accepted,1,0)

        #plaq_t = calculate_Plaquette(U,B,temps)*factor
        #println("$itrj plaq_t = $plaq_t")
        
        if itrj % 10 == 0
            plaq_t = calculate_Plaquette(U,B,temps)*factor
            if get_myrank(U) == 0
                println("$itrj plaq_t = $plaq_t")
            end
            poly = calculate_Polyakov_loop(U,temps) 
            if get_myrank(U) == 0
                println("$itrj polyakov loop = $(real(poly)) $(imag(poly))")
                println("acceptance ratio ",numaccepted/itrj)
            end
        end
    end
    return plaq_t,numaccepted/numtrj

end


function main()
    β = 5.7
    NX = 4
    NY = 4
    NZ = 4
    NT = 4
    NC = 3
    Flux = [0,0,1,1,0,0]
    #HMC_test_4D(NX,NY,NZ,NT,NC,β)
    HMC_test_4D_tHooft(NX,NY,NZ,NT,NC,Flux,β)
end
main()

Utilities

Data structure

We can access the gauge field defined on the bond between two neigbohr points. In 4D system, the gauge field is like u[ic,jc,ix,iy,iz,it]. There are four directions in 4D system. Gaugefields.jl uses the array like:

NX = 4
NY = 4
NZ = 4
NT = 4
Nwing = 1
Dim = 4

U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")

In the later exaples, we use, mu=1 and u=U[mu] as an example.

Hermitian conjugate (Adjoint operator)

If you want to get the hermitian conjugate of the gauge fields, you can do like

u'

This is evaluated with the lazy evaluation. So there is no memory copy. This returms $U_\mu^\dagger$ for all sites.

Shift operator

If you want to shift the gauge fields, you can do like

shifted_u = shift_U(u, shift)

This is also evaluated with the lazy evaluation. Here shift is shift=(1,0,0,0) for example.

matrix-field matrix-field product

If you want to calculate the matrix-matrix multiplicaetion on each lattice site, you can do like

As a mathematical expression, for matrix-valued fields A(n), B(n), we define "matrix-field matrix-field product" as,

$$[A(n)B(n)]_{ij} = \sum_k [A(n)]_{ik} [B(n)]_{kj}$$

for all site index n.

In our package, this is expressed as,

mul!(C,A,B)

which means C = A*B on each lattice site. Here A, B, C are same type of u.

Trace operation

If you want to calculate the trace of the gauge field, you can do like

tr(A)

It is useful to evaluation actions. This trace operation summing up all indecis, spacetime and color.

Applications

This package and Wilsonloop.jl enable you to perform several calculations. Here we demonstrate them.

Some of them will be simplified in LatticeQCD.jl.

Wilson loops

We develop Wilsonloop.jl, which is useful to calculate Wilson loops. If you want to use this, please install like

add Wilsonloop.jl

For example, if you want to calculate the following quantity:

$$U_{1}(n)U_{2}(n+\hat{1}) U^{\dagger}_{1}(n+\hat{2}) U^{\dagger}_2(n) e^{-2\pi B_{12}(n) / N} ,$$

which is Z(Nc) 1-form gauge invariant [arXiv:2303.10977 [hep-lat]].

You can use Wilsonloop.jl as follows

using Wilsonloop
loop = [(1,1),(2,1),(1,-1),(2,-1)]
w = Wilsonline(loop)

The output is L"$U_{1}(n)U_{2}(n+e_{1})U^{\dagger}_{1}(n+e_{2})U^{\dagger}_{2}(n)$". Then, you can evaluate this loop with the use of the Gaugefields.jl like:

using LinearAlgebra
NX = 4
NY = 4
NZ = 4
NT = 4
NC = 3
Nwing = 0
Dim = 4
U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")

flux=[1,0,0,0,0,1] # FLUX=[Z12,Z13,Z14,Z23,Z24,Z34]
B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NY,condition = "tflux")

temp1 = similar(U[1])
temp2 = similar(U[1])
temp3 = similar(U[1])
temp4 = similar(U[1])
V = similar(U[1])

evaluate_gaugelinks!(V,w,U,B,[temp1,temp2,temp3,temp4])
println(tr(V))

For example, if you want to calculate the clover operators, you can define like:

function make_cloverloop(μ,ν,Dim)
    loops = Wilsonline{Dim}[]
    loop_righttop = Wilsonline([(μ,1),(ν,1),(μ,-1),(ν,-1)],Dim = Dim) # Pmunu
    push!(loops,loop_righttop)
    loop_rightbottom = Wilsonline([(ν,-1),(μ,1),(ν,1),(μ,-1)],Dim = Dim) # Qmunu
    push!(loops,loop_rightbottom)
    loop_leftbottom= Wilsonline([(μ,-1),(ν,-1),(μ,1),(ν,1)],Dim = Dim) # Rmunu
    push!(loops,loop_leftbottom)
    loop_lefttop = Wilsonline([(ν,1),(μ,-1),(ν,-1),(μ,1)],Dim = Dim) # Smunu
    push!(loops,loop_lefttop)
    return loops
end

Calculating actions

We can calculate actions from this packages with fixed gauge fields U. We introduce the concenpt "Scalar-valued neural network", which is S(U) -> V, where U and V are gauge fields.

using Gaugefields
using LinearAlgebra
function test1()
    NX = 4
    NY = 4
    NZ = 4
    NT = 4
    Nwing = 0
    Dim = 4
    NC = 3
    flux=[1,0,0,0,0,1]

    U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
    B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NY,condition = "tflux")


    gauge_action = GaugeAction(U,B) #empty network
    plaqloop = make_loops_fromname("plaquette") #This is a plaquette loops. 
    append!(plaqloop,plaqloop') #We need hermitian conjugate loops for making the action real. 
    β = 1 #This is a coefficient.
    push!(gauge_action,β,plaqloop)
    
    show(gauge_action)

    Uout = evaluate_GaugeAction_untraced(gauge_action,U,B)
    println(tr(Uout))
end

test1()

The output is

----------------------------------------------
Structure of the actions for Gaugefields
num. of terms: 1
-------------------------------
      1-st term: 
          coefficient: 1.0
      -------------------------
1-st loop
L"$U_{1}(n)U_{2}(n+e_{1})U^{\dagger}_{1}(n+e_{2})U^{\dagger}_{2}(n)$"	
2-nd loop
L"$U_{1}(n)U_{3}(n+e_{1})U^{\dagger}_{1}(n+e_{3})U^{\dagger}_{3}(n)$"	
3-rd loop
L"$U_{1}(n)U_{4}(n+e_{1})U^{\dagger}_{1}(n+e_{4})U^{\dagger}_{4}(n)$"	
4-th loop
L"$U_{2}(n)U_{3}(n+e_{2})U^{\dagger}_{2}(n+e_{3})U^{\dagger}_{3}(n)$"	
5-th loop
L"$U_{2}(n)U_{4}(n+e_{2})U^{\dagger}_{2}(n+e_{4})U^{\dagger}_{4}(n)$"	
6-th loop
L"$U_{3}(n)U_{4}(n+e_{3})U^{\dagger}_{3}(n+e_{4})U^{\dagger}_{4}(n)$"	
7-th loop
L"$U_{2}(n)U_{1}(n+e_{2})U^{\dagger}_{2}(n+e_{1})U^{\dagger}_{1}(n)$"	
8-th loop
L"$U_{3}(n)U_{1}(n+e_{3})U^{\dagger}_{3}(n+e_{1})U^{\dagger}_{1}(n)$"	
9-th loop
L"$U_{4}(n)U_{1}(n+e_{4})U^{\dagger}_{4}(n+e_{1})U^{\dagger}_{1}(n)$"	
10-th loop
L"$U_{3}(n)U_{2}(n+e_{3})U^{\dagger}_{3}(n+e_{2})U^{\dagger}_{2}(n)$"	
11-th loop
L"$U_{4}(n)U_{2}(n+e_{4})U^{\dagger}_{4}(n+e_{2})U^{\dagger}_{2}(n)$"	
12-th loop
L"$U_{4}(n)U_{3}(n+e_{4})U^{\dagger}_{4}(n+e_{3})U^{\dagger}_{3}(n)$"	
      -------------------------
----------------------------------------------
8928.0 + 0.0im

Fractional topological charge and so on

We can calculate the topological charge and energy density by using gradient flow as

temps = Temporalfields(U[1], num=9)

U_copy = similar(U)
B_copy = similar(B)
temp_UμνTA= Matrix{typeof(U[1])}(undef,Dim,Dim)
# for calc energy density
W_temp = Matrix{typeof(U[1])}(undef,Dim,Dim)
for μ=1:Dim
    for ν=1:Dim
        W_temp[μ,ν] = similar(U[1])
    end
end

calc_Q_gradflow!(U_copy,U,temp_UμνTA,W_temp,temps,conditions=["Qclover","Qimproved","Eclover","Energydensity"])

or

calc_Q_gradflow!(U_copy,B_copy,U,B,temp_UμνTA,W_temp,temps,conditions=["Qclover","Qimproved","Eclover","Energydensity"])

Then,

Flowtime 1.0
Qclover:       0.1591786559310214 - 0.0im
Qimproved:     0.17536509762551222 + 0.0im
Eclover:       0.09954804832666195 - 0.0im
Energydensity: 0.09954804832666195

Conditions are "Qplaq", "Qclover", "Qimproved", "Eplaq", "Eclover", "Energydensity".

Appendix: How to calculate derivatives

We can easily calculate the matrix derivative of the actions. The matrix derivative is defined as

$$\frac{\partial S}{\partial U_{\mu}(n)}]_{ij} = \frac{\partial S}{\partial U_{\mu,ji}(n)}$$

We can calculate this like

dSdUμ = calc_dSdUμ(gauge_action,μ,U,B)

or

calc_dSdUμ!(dSdUμ,gauge_action,μ,U,B)

Hybrid Monte Carlo

With the use of the matrix derivative, we can do the Hybrid Monte Carlo method. The simple code is as follows.

using Gaugefields
using LinearAlgebra

function MDtest!(gauge_action,U,B,Dim)
    p = initialize_TA_Gaugefields(U) #This is a traceless-antihermitian gauge fields. This has NC^2-1 real coefficients. 
    Uold = similar(U)
    substitute_U!(Uold,U)
    MDsteps = 50
    temp1 = similar(U[1])
    temp2 = similar(U[1])
    comb = 6
    factor = 1/(comb*U[1].NV*U[1].NC)
    numaccepted = 0

    numtrj = 100
    for itrj = 1:numtrj
        accepted = MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold)
        numaccepted += ifelse(accepted,1,0)

        plaq_t = calculate_Plaquette(U,B,temp1,temp2)*factor
        println("$itrj plaq_t = $plaq_t")
        println("acceptance ratio ",numaccepted/itrj)
    end
end

We define the functions as

function calc_action(gauge_action,U,B,p)
    NC = U[1].NC
    Sg = -evaluate_GaugeAction(gauge_action,U,B)/NC
    Sp = p*p/2
    S = Sp + Sg
    return real(S)
end

function MDstep!(gauge_action,U,B,p,MDsteps,Dim,Uold)
    Δτ = 1.0/MDsteps
    gauss_distribution!(p)
    Sold = calc_action(gauge_action,U,B,p)
    substitute_U!(Uold,U)

    for itrj=1:MDsteps
        U_update!(U,p,0.5,Δτ,Dim,gauge_action)

        P_update!(U,B,p,1.0,Δτ,Dim,gauge_action)

        U_update!(U,p,0.5,Δτ,Dim,gauge_action)
    end
    Snew = calc_action(gauge_action,U,B,p)
    println("Sold = $Sold, Snew = $Snew")
    println("Snew - Sold = $(Snew-Sold)")
    ratio = min(1,exp(-Snew+Sold))
    if rand() > ratio
        substitute_U!(U,Uold)
        return false
    else
        return true
    end
end

function U_update!(U,p,ϵ,Δτ,Dim,gauge_action)
    temps = get_temporary_gaugefields(gauge_action)
    temp1 = temps[1]
    temp2 = temps[2]
    expU = temps[3]
    W = temps[4]

    for μ=1:Dim
        exptU!(expU,ϵ*Δτ,p[μ],[temp1,temp2])
        mul!(W,expU,U[μ])
        substitute_U!(U[μ],W)
        
    end
end

function P_update!(U,B,p,ϵ,Δτ,Dim,gauge_action) # p -> p +factor*U*dSdUμ
    NC = U[1].NC
    temp  = gauge_action._temp_U[end]
    dSdUμ = similar(U[1])
    factor =  -ϵ*Δτ/(NC)

    for μ=1:Dim
        calc_dSdUμ!(dSdUμ,gauge_action,μ,U,B)
        mul!(temp,U[μ],dSdUμ) # U*dSdUμ
        Traceless_antihermitian_add!(p[μ],factor,temp)
    end
end

Then, we can do the HMC:

function test1()
    NX = 4
    NY = 4
    NZ = 4
    NT = 4
    Nwing = 0
    Dim = 4
    NC = 3
    flux=[1,0,0,0,0,1]

    U = Initialize_Gaugefields(NC,Nwing,NX,NY,NZ,NT,condition = "cold")
    B = Initialize_Bfields(NC,flux,Nwing,NX,NY,NZ,NY,condition = "tflux")


    gauge_action = GaugeAction(U,B)
    plaqloop = make_loops_fromname("plaquette")
    append!(plaqloop,plaqloop') # add hermitian conjugate
    β = 5.7/2 # real part; re[p] = (p+p')/2
    push!(gauge_action,β,plaqloop)
    
    show(gauge_action)

    MDtest!(gauge_action,U,B,Dim)

end

test1()

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README.md

README.md

Gaugefields

Abstract

What this package can do:

Install

Development mode

How to use

Generating configurations and File loading

ILDG format for SU(N) guage fields

Text format for Bridge++

File loading

File saving

Z(N) 2-form gauge fields

Hybrid Monte Carlo

Non-dynamical higher-form gauge fields

Dynamical higher-form gauge fields

Gradient flow

A simple case

Gradient flow with general terms

HMC with MPI

Utilities

Data structure

Hermitian conjugate (Adjoint operator)

Shift operator

matrix-field matrix-field product

Trace operation

Applications

Wilson loops

Calculating actions

Fractional topological charge and so on

Appendix: How to calculate derivatives

Hybrid Monte Carlo

Files

README.md

Latest commit

History

README.md

File metadata and controls

Gaugefields

Abstract

What this package can do:

Install

Development mode

How to use

Generating configurations and File loading

ILDG format for SU(N) guage fields

Text format for Bridge++

File loading

File saving

Z(N) 2-form gauge fields

Hybrid Monte Carlo

Non-dynamical higher-form gauge fields

Dynamical higher-form gauge fields

Gradient flow

A simple case

Gradient flow with general terms

HMC with MPI

Utilities

Data structure

Hermitian conjugate (Adjoint operator)

Shift operator

matrix-field matrix-field product

Trace operation

Applications

Wilson loops

Calculating actions

Fractional topological charge and so on

Appendix: How to calculate derivatives

Hybrid Monte Carlo