Use QUBODrivers

JuliaQUBO · Apr 9, 2023 · 9770ca2 · 9770ca2
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diff --git a/Project.toml b/Project.toml
@@ -1,17 +1,13 @@
-name    = "QiskitOpt"
-uuid    = "81b20daf-e62b-4502-a0d1-aa084de80e33"
-authors = [
-    "pedromxavier <[email protected]>",
-    "pedroripper <[email protected]>",
-]
+name = "QiskitOpt"
+uuid = "81b20daf-e62b-4502-a0d1-aa084de80e33"
+authors = ["pedromxavier <[email protected]>", "pedroripper <[email protected]>"]
 version = "0.1.0"
 
 [deps]
-Anneal     = "e4d9eb7f-b088-426e-aeb5-1c0dae3d8abb"
 PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
-Random     = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+QUBODrivers = "a3f166f7-2cd3-47b6-9e1e-6fbfe0449eb0"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 
 [compat]
-Anneal     = "0.6"
 PythonCall = "0.9"
-julia      = "1.6"
+julia = "1.6"
diff --git a/README.md b/README.md
@@ -1,7 +1,8 @@
 # QiskitOpt.jl
 [![DOI](https://zenodo.org/badge/587349377.svg)](https://zenodo.org/badge/latestdoi/587349377)
+[![QUBODRIVERS](https://img.shields.io/badge/Powered%20by-QUBODrivers.jl-%20%234063d8)](https://github.com/psrenergy/QUBODrivers.jl)
 
-IBM Qiskit Optimization Wrapper for JuMP (ft. [Anneal.jl](https://github.com/psrenergy/Anneal.jl))
+IBM Qiskit Optimization Wrapper for JuMP
 
 ## Installation
 ```julia

diff --git a/src/QAOA.jl b/src/QAOA.jl
@@ -1,15 +1,22 @@
 module QAOA
 
-using Anneal
 using Random
 using PythonCall: pyconvert
 using ..QiskitOpt:
     qiskit,
     qiskit_optimization_algorithms,
     qiskit_optimization_runtime,
     quadratic_program
-
-Anneal.@anew Optimizer begin
+import QUBODrivers:
+    MOI,
+    QUBODrivers,
+    QUBOTools,
+    Sample,
+    SampleSet,
+    @setup,
+    sample
+
+@setup Optimizer begin
     name    = "IBM Qiskit QAOA"
     sense   = :min
     domain  = :bool
@@ -21,7 +28,7 @@ Anneal.@anew Optimizer begin
     end
 end
 
-function Anneal.sample(sampler::Optimizer{T}) where {T}
+function sample(sampler::Optimizer{T}) where {T}
     # -*- Retrieve Attributes - *-
     seed        = MOI.get(sampler, QAOA.RandomSeed())
     num_reads   = MOI.get(sampler, QAOA.NumberOfReads())
@@ -34,10 +41,13 @@ function Anneal.sample(sampler::Optimizer{T}) where {T}
     rng = Random.Xoshiro(seed)
 
     # Results vector
-    samples = Vector{Anneal.Sample{T,Int}}(undef, num_reads)
+    samples = Vector{Sample{T,Int}}(undef, num_reads)
 
     # Timing Information 
-    time_data = Dict{String,Any}()
+    metadata = Dict{String,Any}(
+        "origin" => "IBMQ QAOA @ $(ibm_backend)",
+        "time"   => Dict{String,Any}(), 
+    )
 
     # Connect to IBMQ and get backend
     connect(sampler) do client
@@ -66,20 +76,15 @@ function Anneal.sample(sampler::Optimizer{T}) where {T}
             samples[i] = Sample{T}(Ψ[j], Λ[j])
         end
 
-        time_data["effective"] = pyconvert(
+        metadata["time"]["effective"] = pyconvert(
             Float64,
             results.min_eigen_solver_result.optimizer_time,
         )
 
         return nothing
     end
 
-    metadata = Dict{String,Any}(
-        "origin" => "IBMQ QAOA @ $(ibm_backend)",
-        "time"   => time_data,
-    )
-
-    return Anneal.SampleSet{T}(samples, metadata)
+    return SampleSet{T}(samples, metadata)
 end
 
 function connect(

diff --git a/src/QiskitOpt.jl b/src/QiskitOpt.jl
@@ -1,9 +1,9 @@
 module QiskitOpt
 
-using Anneal
 using PythonCall
+import QUBODrivers: MOI, QUBODrivers, QUBOTools
 
-# -*- :: Python Qiskit Modules :: -*- #
+# :: Python Qiskit Modules ::
 const qiskit                         = PythonCall.pynew()
 const qiskit_algorithms              = PythonCall.pynew()
 const qiskit_optimization            = PythonCall.pynew()
@@ -12,7 +12,7 @@ const qiskit_optimization_runtime    = PythonCall.pynew()
 const qiskit_utils                   = PythonCall.pynew()
 
 function __init__()
-    # -*- Load Python Packages -*- #
+    # Load Python Packages
     PythonCall.pycopy!(qiskit, pyimport("qiskit"))
     PythonCall.pycopy!(qiskit_algorithms, pyimport("qiskit.algorithms"))
     PythonCall.pycopy!(qiskit_optimization, pyimport("qiskit_optimization"))
@@ -23,19 +23,19 @@ function __init__()
     PythonCall.pycopy!(qiskit_optimization_runtime, pyimport("qiskit_optimization.runtime"))
     PythonCall.pycopy!(qiskit_utils, pyimport("qiskit.utils"))
 
-    # -*- IBMQ Credentials -*- #
+    # IBMQ Credentials
     IBMQ_API_TOKEN = get(ENV, "IBMQ_API_TOKEN", nothing)
 
     if !isnothing(IBMQ_API_TOKEN)
         qiskit.IBMQ.save_account(IBMQ_API_TOKEN)
     end
 end
 
-function quadratic_program(sampler::Anneal.AbstractSampler{T}) where {T}
-    # -*- Retrieve Model -*- #
-    Q, α, β = Anneal.qubo(sampler, Dict)
+function quadratic_program(sampler::QUBODrivers.AbstractSampler{T}) where {T}
+    # Retrieve Model
+    Q, α, β = QUBODrivers.qubo(sampler, Dict)
 
-    # -*- Build Qiskit Model -*- #
+    # Build Qiskit Model
     linear    = PythonCall.pydict()
     quadratic = PythonCall.pydict()
 
@@ -49,7 +49,7 @@ function quadratic_program(sampler::Anneal.AbstractSampler{T}) where {T}
 
     qp = qiskit_optimization.QuadraticProgram()
 
-    for v in string.(Anneal.indices(sampler))
+    for v in string.(QUBOTools.indices(sampler))
         qp.binary_var(v)
     end
 

diff --git a/src/VQE.jl b/src/VQE.jl
@@ -1,16 +1,23 @@
 module VQE
 
 using Random
-using Anneal
 using PythonCall: pyconvert
 using ..QiskitOpt:
     qiskit,
     qiskit_optimization_algorithms,
     qiskit_algorithms,
     qiskit_optimization_runtime,
     quadratic_program
-
-Anneal.@anew Optimizer begin
+import QUBODrivers:
+    MOI,
+    QUBODrivers,
+    QUBOTools,
+    Sample,
+    SampleSet,
+    @setup,
+    sample
+
+@setup Optimizer begin
     name    = "VQE @ IBMQ"
     sense   = :min
     domain  = :bool
@@ -26,23 +33,26 @@ Anneal.@anew Optimizer begin
     end
 end
 
-function Anneal.sample(sampler::Optimizer{T}) where {T}
-    # -*- Retrieve Attributes - *-
+function sample(sampler::Optimizer{T}) where {T}
+    # Retrieve Attribute
     seed        = MOI.get(sampler, VQE.RandomSeed())
     num_reads   = MOI.get(sampler, VQE.NumberOfReads())
     ibm_backend = MOI.get(sampler, VQE.IBMBackend())
 
-    # -*- Instantiate Random Generator -*- #
+    # Instantiate Random Generator
     rng = Random.Xoshiro(seed)
 
-    # -*- Retrieve Model -*- #
+    # Retrieve Model
     qp, α, β = quadratic_program(sampler)
 
     # Results vector
-    samples = Vector{Anneal.Sample{T,Int}}(undef, num_reads)
+    samples = Vector{Sample{T,Int}}(undef, num_reads)
 
-    # Timing Information 
-    time_data = Dict{String,Any}()
+    # Extra Information 
+    metadata = Dict{String,Any}(
+        "origin" => "IBMQ VQE @ $(ibm_backend)",
+        "time"   => Dict{String,Any}(), 
+    )
 
     # Connect to IBMQ and get backend
     connect(sampler) do client
@@ -71,51 +81,45 @@ function Anneal.sample(sampler::Optimizer{T}) where {T}
             samples[i] = Sample{T}(Ψ[j], Λ[j])
         end
 
-        time_data["effective"] = pyconvert(
+        metadata["time"]["effective"] = pyconvert(
             Float64,
             results.min_eigen_solver_result.optimizer_time
         )
 
         return nothing
     end
 
-    metadata = Dict{String,Any}(
-        "origin" => "IBMQ @ $(ibm_backend)",
-        "time"   => time_data, 
-    )
-
-
-    return Anneal.SampleSet{T}(samples, metadata)
+    return SampleSet{T}(samples, metadata)
 end
 
 function connect(
     callback::Function,
     sampler::Optimizer,
 )
-    # -*- Retrieve Attributes -*- #
+    # Retrieve Attributes
     num_shots    = MOI.get(sampler, VQE.NumberOfShots())
     max_iter     = MOI.get(sampler, VQE.MaximumIterations())
     num_reps     = MOI.get(sampler, VQE.NumberOfRepetitions())
     num_qubits   = MOI.get(sampler, MOI.NumberOfVariables())
     ibm_backend  = MOI.get(sampler, VQE.IBMBackend())
     entanglement = MOI.get(sampler, VQE.Entanglement())
 
-    # -*- Load Credentials -*- #
+    # Load Credentials
     qiskit.IBMQ.load_account()
 
-    # -*- Connect to provider -*- #
+    # Connect to provider
     provider = qiskit.IBMQ.get_provider()
     backend  = provider.get_backend(ibm_backend)
     SPSA     = qiskit_algorithms.optimizers.SPSA(maxiter = max_iter)
 
-    # -*- Setup Ansatz -*- #
+    # Setup Ansatz
     ansatz = qiskit.circuit.library.EfficientSU2(
         num_qubits   = num_qubits,
         reps         = num_reps,
         entanglement = entanglement,
     )
 
-    # -*- Setup VQE Client -*- #
+    # Setup VQE Client
     client = qiskit_optimization_runtime.VQEClient(
         provider  = provider,
         backend   = backend,

diff --git a/test/runtests.jl b/test/runtests.jl
@@ -1,4 +1,4 @@
-using QiskitOpt
+using QiskitOpt: QUBODrivers, QAOA, VQE
 
-QiskitOpt.QAOA.test(; examples=true)
-QiskitOpt.VQE.test(; examples=true)
+QUBODrivers.test(QAOA.Optimizer)
+QUBODrivers.test(VQE.Optimizer)