Performance Tips

Use the correct block types

`put` v.s. `subroutine`

While both blocks maps a subblock to a subset of qudits, their implementations are purposes are quite different. The put block applies the gate in a in-place manner, which requires the static matrix representation of its subblock. It works the best when the subblock is small.

The subroutine block is for running a sub-program in a subset of qubits. It first sets target qubits as active qubits using the focus! function, then apply the gates on active qubits. Finally, it unsets the active qubits with the relax! function.

julia> using Yao

julia> reg = rand_state(20);

julia> @time apply(reg, put(20, 1:6=>EasyBuild.qft_circuit(6)));  # second run
  0.070245 seconds (1.32 k allocations: 16.525 MiB)

julia> @time apply(reg, subroutine(20, EasyBuild.qft_circuit(6), 1:6));  # second run
  0.036840 seconds (1.07 k allocations: 16.072 MiB)

`repeat` v.s. `put`

repeat block is not only an alias of a chain of put, sometimes it can provide speed ups due to the different implementations.

julia> reg = rand_state(20);

julia> @time apply!(reg, repeat(20, X));
  0.002252 seconds (5 allocations: 656 bytes)

julia> @time apply!(reg, chain([put(20, i=>X) for i=1:20]));
  0.049362 seconds (82.48 k allocations: 4.694 MiB, 47.11% compilation time)

Other gates accelerated by repeat include: X, Y, Z, S, T, Sdag, and Tdag.

$ JULIA_NUM_THREAD=4 julia xxx.jl

Check the Julia Multi-Treading manual for details.

GPU backend

The GPU backend is supported in CuYao.

julia> using Yao, CuYao

julia> reg = CuYao.cu(rand_state(20));

julia> circ = Yao.EasyBuild.qft_circuit(20);

julia> apply!(reg, circ)
ArrayReg{2, ComplexF64, CuArray...}
    active qubits: 20/20
    nlevel: 2

Performance Tips

Use the correct block types

`put` v.s. `subroutine`

`repeat` v.s. `put`

Diagonal matrix in `time_evole`

Register storage

Multithreading

GPU backend