We present on the activities at DLR to implement a software library as an interface to the quantum annealers, such as D-Wave's, to enable researchers to run automated and reproducable experiments fast

Lobe, Elisabeth

Institute of Transport Research:Publications

1st Working Symposium on Quantum Software EngineeringDLRKnowledge for TomorrowProgramming Quantum AnnealersDr. Elisabeth LobeHigh-Performance ComputingInstitute for Software TechnologyDLR German Aerospace Center15th December 2022dlr.de · Slide 1 of 5 > Elisabeth Lobe · Programming Quantum Annealers > 1st Working Symposium on Quantum Software Engineering > 15/12/2022Optimizer for Ising ProblemsMinimizationover spin variablesof a quadratic objective functionwithout further constraints“Programming”providing the weights 𝑊 ∈ R𝑉and strengths 𝑆 ∈ R𝐸 forgraph 𝐺 = (𝑉,𝐸)min∑︁𝑣∈𝑉𝑊𝑣 𝑠𝑣 +∑︁𝑣𝑤∈𝐸𝑆𝑣𝑤 𝑠𝑣𝑠𝑤s.t. 𝑠 ∈ {−1,+1}𝑉-4-7-1433-2412-3-140000000000000-4-71-4-3-3-2-41-2-3-1-4NP-hardDLRdlr.de · Slide 2 of 5 > Elisabeth Lobe · Programming Quantum Annealers > 1st Working Symposium on Quantum Software Engineering > 15/12/2022Restricted HardwareNo ideal realization of underlying physical conceptheuristic solverno guarantee for optimal solutionHardware structure of the quantum chipformed by overlapping superconducting loopsrealizes specific graphs(Chimera in DW2000Q, Pegasus in Advantage5)with certain “broken” qubits due to calibrationResolution of input parameterslimited due to analog control circuitsparameter distribution decisively influences success probabilityDLRdlr.de · Slide 3 of 5 > Elisabeth Lobe · Programming Quantum Annealers > 1st Working Symposium on Quantum Software Engineering > 15/12/2022Layers of Abstractiondiscreteoptimizationproblemsolutionmodelproblemsolution“real worldproblem”applicationphysicalsystemmodelphysicalrealizationresult outputquantumannealingIsingmodelIsingproblemsolutionInterfacerestrictedIsing modelrestrictedIsing problemsolution** embedding and parameter setting“real worldproblem”modelproblemdiscreteoptimizationproblemIsingproblemrestrictedIsing modelrestrictedIsing problemapplication solution solution solution solution result outputquantumannealingInterfaceDLRdlr.de · Slide 4 of 5 > Elisabeth Lobe · Programming Quantum Annealers > 1st Working Symposium on Quantum Software Engineering > 15/12/2022GoalsEasily reproducible experimentsreformulate arbitrary optimization problems to Ising problemsby automated transformation stepsbased on a parameterized formulation of the problem instancesgenerate several problem instances with the same structurestore or load intermediate data at every stage of the transformation processAllow analysis of the machine behaviour and experimental results |provide hints whether problems are suitable to be solved with the annealersprovide solutions of classical solver for comparisonModularity for flexible usage 3adapt to different hardware architectures and thus restrictionsbase for further algorithmic implementationsDLRdlr.de · Slide 5 of 5 > Elisabeth Lobe · Programming Quantum Annealers > 1st Working Symposium on Quantum Software Engineering > 15/12/2022Our Software Libraryquappsmin∑︁𝑐∈𝐶∑︁𝑛𝑚∈𝐸𝑥𝑛𝑐𝑥𝑚𝑐s.t.∑︁𝑐∈𝐶𝑥𝑛𝑐 = 1 ∀𝑛 ∈ 𝑁,𝑥 ∈ {0, 1}𝑁×𝐶Application Layerquarkmin∑︁𝑣∈𝑉𝑊𝑣𝑠𝑣+∑︁𝑣𝑤∈𝐸𝑆𝑣𝑤𝑠𝑣𝑠𝑤s.t. 𝑠 ∈ {−1,+1}𝑉Transformation LayerquokkaQAroqueQAOAQC Interface Layerproblemparametersproblemdescription(cost function,constraints)restrictedIsing problemcheckvalidityDLRDLRKnowledge for TomorrowQuestions? CodeDr. Elisabeth LobeHigh-Performance Computinghttps://gitlab.com/quantum-computing-software/quarkInstitute for Software TechnologyDLR German Aerospace Centerelisabeth.lobe@dlr.de

Programming Quantum Annealers

https://elib.dlr.de/192617/1/Lobe_Quantum_Annealing_Handout.pdf

Programming Quantum Annealers

Abstract

Similar works

Full text

Available Versions

Institute of Transport Research:Publications