EDKit.jl

Julia package for general many-body exact diagonalization calculation.
Author jayren3996
Github
Popularity
7 Stars
Updated Last
1 Year Ago
Started In
April 2021

EDKit.jl

Julia package for general many-body exact diagonalization calculation. The package provide a general Hamiltonian constructing routine for specific symmetry sectors. The functionalities can be extended with user-defined bases.

Installation

Run the following script in the Pkg REPL environment:

pkg> add EDKit

Examples

XXZ Model with Random Field

Consider the Hamiltonian

$$H = \sum_i\left(\sigma_i^x \sigma^x_{i+1} + \sigma^y_i\sigma^y_{i+1} + h_i \sigma^z_i\sigma^z_{i+1}\right).$$

We choose the system size to be L=10. The Hamiltonian need 3 generic information:

  1. Local operators represented by matrices;
  2. Site indices where each local operator acts on;
  3. Basis, if use the default tensor-product basis, only need to provide the system size.

The following script generate the information we need to generate XXZ Hamiltonian:

L = 10
mats = [
    fill(spin("XX"), L);
    fill(spin("YY"), L);
    [randn() * spin("ZZ") for i=1:L]
]
inds = [
    [[i, mod(i, L)+1] for i=1:L];
    [[i, mod(i, L)+1] for i=1:L];
    [[i, mod(i, L)+1] for i=1:L]
]
H = operator(mats, inds, L)

Then we can use the constructor operator to create Hamiltonian:

julia> H = operator(mats, inds, L)
Operator of size (1024, 1024) with 10 terms.

The constructor return an Operator object, which is a linear operator that can act on vector/ matrix. For example, we can act H on the ferromagnetic state:

julia> ψ = zeros(2^L); ψ[1] = 1; H * random_state
1024-element Vector{Float64}:
 -1.5539463277491536
  5.969061189628827
  3.439873269795492
  1.6217619009059376
  0.6101231697221667
  6.663735992405236
  ⋮
  5.517409105968883
  0.9498121684380652
 -0.0004996659995972763
  2.6020967735388734
  4.99027405325114
 -1.4831032210847952

If we need a matrix representation of the Hamitonian, we can convert H to julia array by:

julia> Array(H)
1024×1024 Matrix{Float64}:
 -1.55617  0.0       0.0       0.0     …   0.0      0.0       0.0
  0.0      4.18381   2.0       0.0         0.0      0.0       0.0
  0.0      2.0      -1.42438   0.0         0.0      0.0       0.0
  0.0      0.0       0.0      -1.5901      0.0      0.0       0.0
  0.0      0.0       2.0       0.0         0.0      0.0       0.0
  0.0      0.0       0.0       2.0     …   0.0      0.0       0.0
  ⋮                                    ⋱                     
  0.0      0.0       0.0       0.0         0.0      0.0       0.0
  0.0      0.0       0.0       0.0         2.0      0.0       0.0
  0.0      0.0       0.0       0.0     …   0.0      0.0       0.0
  0.0      0.0       0.0       0.0        -1.42438  2.0       0.0
  0.0      0.0       0.0       0.0         2.0      4.18381   0.0
  0.0      0.0       0.0       0.0         0.0      0.0      -1.55617

Or use the function sparse to create the sparse matrix (requires the module SparseArrays being imported):

julia> sparse(H)
1024×1024 SparseMatrixCSC{Float64, Int64} with 6144 stored entries:
⠻⣦⣄⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠳⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⢹⡻⣮⡳⠄⢠⡀⠀⠀⠀⠀⠀⠀⠈⠳⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠙⠎⢿⣷⡀⠙⢦⡀⠀⠀⠀⠀⠀⠀⠈⠳⣄⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠲⣄⠈⠻⣦⣄⠙⠀⠀⠀⢦⡀⠀⠀⠀⠈⠳⣄⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠈⠳⣄⠙⡻⣮⡳⡄⠀⠀⠙⢦⡀⠀⠀⠀⠈⠳⣄⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠙⠮⢻⣶⡄⠀⠀⠀⠙⢦⡀⠀⠀⠀⠈⠳⣄⠀
⢤⡀⠀⠀⠀⠀⠠⣄⠀⠀⠀⠉⠛⣤⣀⠀⠀⠀⠙⠂⠀⠀⠀⠀⠈⠓
⠀⠙⢦⡀⠀⠀⠀⠈⠳⣄⠀⠀⠀⠘⠿⣧⡲⣄⠀⠀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠙⢦⡀⠀⠀⠀⠈⠳⣄⠀⠀⠘⢮⡻⣮⣄⠙⢦⡀⠀⠀⠀⠀
⠀⠀⠀⠀⠀⠙⢦⡀⠀⠀⠀⠈⠳⠀⠀⠀⣄⠙⠻⣦⡀⠙⠦⠀⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠙⢦⡀⠀⠀⠀⠀⠀⠀⠈⠳⣄⠈⢿⣷⡰⣄⠀⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⢦⡀⠀⠀⠀⠀⠀⠀⠈⠃⠐⢮⡻⣮⣇⠀
⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⢦⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠙⠻⣦

Solving AKLT Model Using Symmetries

Consider the AKLT model

$$H = \sum_i\left[\vec S_i \cdot \vec S_{i+1} + \frac{1}{3}\left(\vec S_i \cdot \vec S_{i+1}\right)^2\right],$$

with system size chosen to be L=8. The Hamiltonian operator for this translational-invariant Hamiltonian can be constructed using the trans_inv_operator function:

L = 8
SS = spin((1, "xx"), (1, "yy"), (1, "zz"), D=3)
mat = SS + 1/3 * SS^2
H = trans_inv_operator(mat, 1:2, L)

The second input specifies the indices the operators act on.

Because of the translational symmetry, we can simplify the problem by considering the symmetry. We construct a translational-symmetric basis by:

B = TranslationalBasis(L=8, k=8, base=3)

Here, L is the length of the system, and k labels the momentum k = 0,...,L-1 (integer multiply of 2π/L). The function TranslationalBasis return a basis object containing 834 states. We can obtain the Hamiltonian in this sector by:

julia> H = trans_inv_operator(mat, 1:2, B)
Operator of size (834, 834) with 8 terms.

In addition, we can take into account the total S^z conservation, by constructing the basis

B = TranslationalBasis(L=8, N=8, k=0, base=3)

where the N is the filling number with respect to all-spin-down state. N=L means we select those states whose total Sz equalls 0 (note that we use 0,1,2 to label the Sz=1,0,-1 states). This gives a further reduced Hamiltonian matrix:

julia> H = trans_inv_operator(mat, 1:2, B)
Operator of size (142, 142) with 8 terms.

We can go on step further by considering the spatial reflection symmetry.

B = TranslationParityBasis(L=8, N=0, k=0, p=1, base=3)

where the p argument is the parity p = ±1.

julia> H = trans_inv_operator(mat, 1:2, B)
Operator of size (84, 84) with 8 terms.

PXP Model and Entanglement Entropy

Consider the PXP model

$$H = \sum_i \left(P^0_{i-1} \sigma^x_i P^0_{i+1}\right).$$

Note that the model is defined on the Hilbert space where there is no local |↑↑⟩ configuration. For system size L=20 and in sector k=0,p=+1, the Hamiltonian is constructed by:

mat = begin
    P = [1 0; 0 0]
    kron(P, spin("X"), P)
end
pxpf(v::Vector{<:Integer}) = all(v[i]==0 || v[mod(i, length(v))+1]==0 for i=1:length(v))
basis = TranslationParityBasis(L=20, f=pxpf, k=0, p=1)
H = trans_inv_operator(mat, 2, basis)

where f augument is the selection function for the basis state that can be user defined. We can then diagonalize the Hamiltonian. The bipartite entanglement entropy for each eigenstates can be computed by

vals, vecs = Array(H) |> Hermitian |> eigen
EE = [ent_S(vecs[:,i], 1:L÷2, basis) for i=1:size(basis,1)]
scatter(vals, EE, xlabel="E",ylabel="S",legend=false)

The plot is

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