QuantumToolbox.jl

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Extension for CUDA.jl ​

Introduction ​

This is an extension to support QuantumObject.data conversion from standard dense and sparse CPU arrays to GPU (CUDA.jl) arrays.

This extension will be automatically loaded if user imports both QuantumToolbox.jl and CUDA.jl:

julia
using QuantumToolbox
using CUDA
using CUDA.CUSPARSE
CUDA.allowscalar(false) # Avoid unexpected scalar indexing

We wrapped several functions in CUDA and CUDA.CUSPARSE in order to not only converting QuantumObject.data into GPU arrays, but also changing the element type and word size (32 and 64) since some of the GPUs perform better in 32-bit. The functions are listed as follows (where input A is a QuantumObject):

  • cu(A; word_size=64): return a new QuantumObject with CUDA arrays and specified word_size.

  • CuArray(A): If A.data is a dense array, return a new QuantumObject with CUDA.CuArray.

  • CuArray{T}(A): If A.data is a dense array, return a new QuantumObject with CUDA.CuArray under element type T.

  • CuSparseVector(A): If A.data is a sparse vector, return a new QuantumObject with CUDA.CUSPARSE.CuSparseVector.

  • CuSparseVector{T}(A): If A.data is a sparse vector, return a new QuantumObject with CUDA.CUSPARSE.CuSparseVector under element type T.

  • CuSparseMatrixCSC(A): If A.data is a sparse matrix, return a new QuantumObject with CUDA.CUSPARSE.CuSparseMatrixCSC.

  • CuSparseMatrixCSC{T}(A): If A.data is a sparse matrix, return a new QuantumObject with CUDA.CUSPARSE.CuSparseMatrixCSC under element type T.

  • CuSparseMatrixCSR(A): If A.data is a sparse matrix, return a new QuantumObject with CUDA.CUSPARSE.CuSparseMatrixCSR.

  • CuSparseMatrixCSR{T}(A): If A.data is a sparse matrix, return a new QuantumObject with CUDA.CUSPARSE.CuSparseMatrixCSR under element type T.

We suggest to convert the arrays from CPU to GPU memory by using the function cu because it allows different data-types of input QuantumObject.

Here are some examples:

Converting dense arrays ​

julia
V = fock(2, 0) # CPU dense vector
Quantum Object:   type=Ket()   dims=[2]   size=(2,)
2-element Vector{ComplexF64}:
 1.0 + 0.0im
 0.0 + 0.0im
julia
cu(V)
Quantum Object:   type=Ket()   dims=[2]   size=(2,)
2-element CuArray{ComplexF64, 1, CUDA.DeviceMemory}:
 1.0 + 0.0im
 0.0 + 0.0im
julia
cu(V; word_size = 32)
Quantum Object:   type=Ket()   dims=[2]   size=(2,)
2-element CuArray{ComplexF32, 1, CUDA.DeviceMemory}:
 1.0 + 0.0im
 0.0 + 0.0im
julia
M = Qobj([1 2; 3 4]) # CPU dense matrix
Quantum Object:   type=Operator()   dims=[2]   size=(2, 2)   ishermitian=false
2×2 Matrix{Int64}:
 1  2
 3  4
julia
cu(M)
Quantum Object:   type=Operator()   dims=[2]   size=(2, 2)   ishermitian=false
2×2 CuArray{Int64, 2, CUDA.DeviceMemory}:
 1  2
 3  4
julia
cu(M; word_size = 32)
Quantum Object:   type=Operator()   dims=[2]   size=(2, 2)   ishermitian=false
2×2 CuArray{Int32, 2, CUDA.DeviceMemory}:
 1  2
 3  4

Converting sparse arrays ​

julia
V = fock(2, 0; sparse=true) # CPU sparse vector
Quantum Object:   type=Ket()   dims=[2]   size=(2,)
2-element SparseVector{ComplexF64, Int64} with 1 stored entry:
  [1]  =  1.0+0.0im
julia
cu(V)
Quantum Object:   type=Ket()   dims=[2]   size=(2,)
2-element CuSparseVector{ComplexF64, Int32} with 1 stored entry:
  [1]  =  1.0+0.0im
julia
cu(V; word_size = 32)
Quantum Object:   type=Ket()   dims=[2]   size=(2,)
2-element CuSparseVector{ComplexF32, Int32} with 1 stored entry:
  [1]  =  1.0+0.0im
julia
M = sigmax() # CPU sparse matrix
Quantum Object:   type=Operator()   dims=[2]   size=(2, 2)   ishermitian=true
2×2 SparseMatrixCSC{ComplexF64, Int64} with 2 stored entries:
     ⋅      1.0+0.0im
 1.0+0.0im      ⋅
julia
cu(M)
Quantum Object:   type=Operator()   dims=[2]   size=(2, 2)   ishermitian=true
2×2 CuSparseMatrixCSC{ComplexF64, Int32} with 2 stored entries:
     ⋅      1.0+0.0im
 1.0+0.0im      ⋅
julia
cu(M; word_size = 32)
Quantum Object:   type=Operator()   dims=[2]   size=(2, 2)   ishermitian=true
2×2 CuSparseMatrixCSC{ComplexF32, Int32} with 2 stored entries:
     ⋅      1.0+0.0im
 1.0+0.0im      ⋅