PyBraket.jl is not an officially supported AWS product.
This package provides Julia-Python interoperability between Braket.jl and Python features of the Amazon Braket SDK, such as the Amazon Braket Local Simulators and support for unpickling the results of Braket Jobs.
This is experimental software, and support may be discontinued in the future. For a fully supported SDK, please use the Python SDK. We may change, remove, or deprecate parts of the API when making new releases. Please review the CHANGELOG for information about changes in each release.
You'll need PythonCall.jl installed to use this package.
PyBraket.jl now supports CondaPkg.jl! The package and its tests come with CondaPkg.toml files ready to go.
CondaPkg.jl will install all necessary dependencies for you.
If you want to use an existing Python installation (a virtual environment you control, for example),
you will need a working installation of the Amazon Braket SDK.
You can install the Amazon Braket SDK and all its dependencies through pip as documented on its README.
To use an existing installation through PyBraket.jl you will need to turn off CondaPkg.jl.
In particular, if you already have the SDK and its dependencies installed, you can run:
export JULIA_PYTHONCALL_EXE=path_to_your_python
or add it to your .bash_profile/.bashrc/.zsh_profile/.zshrc/etc.
This will prevent PythonCall.jl and CondaPkg.jl from trying to reinstall the SDK and its dependencies.
Note that if you installed the Amazon Braket SDK in a Python virtual environment, you will need to set this environment variable to the python executable associated with that virtual environment. You can find the path to that python executable by activating the virtenv and running which python if you're on Linux or MacOS.
Running a Local Job:
using PyBraket, Braket
sm = joinpath(@__DIR__, "algo_script", "algo_script.py")
job = LocalJob("local:braket/braket.local.qubit", source_module=sm)
@assert state(job) == "COMPLETED" Running an analog Hamiltonian simulation on the local AHS simulator:
using Braket, PyBraket
using Braket: AtomArrangement, AtomArrangementItem, TimeSeries, DrivingField, AwsDevice, AnalogHamiltonianSimulation, discretize, AnalogHamiltonianSimulationQuantumTaskResult
a = 5.5e-6
register = AtomArrangement()
push!(register, AtomArrangementItem((0.5, 0.5 + 1/√2) .* a))
push!(register, AtomArrangementItem((0.5 + 1/√2, 0.5) .* a))
push!(register, AtomArrangementItem((0.5 + 1/√2, -0.5) .* a))
push!(register, AtomArrangementItem((0.5, -0.5 - 1/√2) .* a))
push!(register, AtomArrangementItem((-0.5, -0.5 - 1/√2) .* a))
push!(register, AtomArrangementItem((-0.5 -1/√2, -0.5) .* a))
push!(register, AtomArrangementItem((-0.5 -1/√2, 0.5) .* a))
push!(register, AtomArrangementItem((-0.5, 0.5 + 1/√2) .* a))
# extracted from device paradigm
(C6, Ω_min, Ω_max, Ω_slope_max, Δ_min, Δ_max, time_max) = (5.42e-24, 0.0, 6.3e6, 2.5e14, -1.25e8, 1.25e8, 4.0e-6)
time_max = Float64(time_max)
Δ_start = -5 * Float64(Ω_max)
Δ_end = 5 * Float64(Ω_max)
@test all(Δ_min <= Δ <= Δ_max for Δ in (Δ_start, Δ_end))
time_ramp = 1e-7 # seconds
@test Float64(Ω_max) / time_ramp < Ω_slope_max
Ω = TimeSeries()
Ω[0.0] = 0.0
Ω[time_ramp] = Ω_max
Ω[time_max - time_ramp] = Ω_max
Ω[time_max] = 0.0
Δ = TimeSeries()
Δ[0.0] = Δ_start
Δ[time_ramp] = Δ_start
Δ[time_max - time_ramp] = Δ_end
Δ[time_max] = Δ_end
ϕ = TimeSeries()
ϕ[0.0] = 0.0
ϕ[time_max] = 0.0
drive = DrivingField(Ω, ϕ, Δ)
ahs_program = AnalogHamiltonianSimulation(register, [drive])
ahs_local = LocalSimulator("braket_ahs")
local_result = result(run(ahs_local, ahs_program, shots=1_000))