IBM Qiskit Optimization Wrapper for JuMP
julia> import Pkg
julia> Pkg.add("QiskitOpt")using JuMP
using QiskitOpt
# Using QAOA
model = Model(QiskitOpt.QAOA.Optimizer)
# Using VQE
model = Model(QiskitOpt.VQE.Optimizer)
Q = [
-1 2 2
2 -1 2
2 2 -1
]
@variable(model, x[1:3], Bin)
@objective(model, Min, x' * Q * x)
optimize!(model)
for i = 1:result_count(model)
xi = value.(x; result=i)
yi = objective_value(model; result=i)
println("f($xi) = $yi")
end# Number of shots
MOI.set(model, VQE.NumberOfReads(), 1000) # or QAOA.NumberOfReads
# Maximum optimizer iterations
MOI.set(model, VQE.MaximumIterations(), 100) # or QAOA.MaximumIterations
# Ansatz
MOI.set(model, VQE.Ansatz(), QiskitOpt.qiskit.circuit.library.EfficientSU2) # or QAOA.Ansatz
# Number of QAOA ansatz repetitions (for QAOA only)
MOI.set(model, QAOA.NumberOfLayers(), 5) MOI.set(model, VQE.IBMBackend(), "ibm_osaka") # or QAOA.IBMBackend
MOI.set(model, VQE.Instance(), "my/instance") # or QAOA.Instance
# Using a fake backend
MOI.set(model, VQE.IBMFakeBackend(), QiskitOpt.qiskit_ibm_runtime.fake_provider.FakeAlgiers) # or QAOA.IBMFakeBackend
List of fake backends available: Qiskit Documentation
To access IBM's Quantum Computers, it is necessary to create an account at IBM Q to obtain an API Token and run the following python code:
from qiskit_ibm_runtime import QiskitRuntimeService
QiskitRuntimeService.save_account(channel='ibm_quantum', token="YOUR_TOKEN_HERE")Another option is to set the IBMQ_API_TOKEN environment variable before loading QiskitOpt.jl:
$ export IBMQ_API_TOKEN=YOUR_TOKEN_HERE
$ julia
julia> using QiskitOptDisclaimer: The IBM Qiskit Optimization Wrapper for Julia is not officially supported by IBM. If you are a commercial customer interested in official support for Julia from IBM, let them know!
Note: If you are using QiskitOpt.jl in your project, we recommend you to include the .CondaPkg entry in your .gitignore file. The PythonCall module will place a lot of files in this folder when building its Python environment.