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SUMMARY:Quantum Optimization Benchmarking Library – The Intractable Decat
 hlon
DESCRIPTION:\nTitle: Quantum Optimization Benchmarking Library – The Intr
 actable Decathlon\nAbstract: Optimization is the methodological backbone b
 ehind the operations of major industrial areas\, including Logistics\, Man
 ufacturing\, Power Systems\, and Process Systems Engineering (PSE) [1]\, a
 mong others. In the face of numerous challenging Combinatorial Optimizatio
 n problems\, the development of novel\, specialized hardware has been an a
 ctive research area over the past decade\, leading to fundamental advancem
 ents in Quantum Computing and other Physics-inspired devices and algorithm
 s [2]. Optimization is a well-established domain in quantum applications r
 esearch\, particularly for existing and near-term quantum hardware [3]. Th
 e field is driven by heuristic quantum algorithms compatible with the hard
 ware\, such as the Quantum Approximate Optimization Algorithm\, or QAOA [4
 ]. Recent hardware improvements pave the way for thorough benchmarking of 
 heuristic quantum algorithms at scale [5].\nThis work presents ten optimiz
 ation problem classes that are difficult for existing classical algorithms
  and can be linked to practically relevant applications\, aiming to enable
  a fair\, comparable\, and meaningful benchmarking effort for quantum opti
 mization methods. The initial compilation contains instances of the proble
 ms known as Market Split\, Low Autocorrelation Binary Sequences\, Minimum 
 Birkhoff Decomposition\, Steiner Tree Packing\, Sports Tournament Scheduli
 ng\, Portfolio Optimization\, Independent Set\, Network Design\, Vehicle R
 outing Problem\, and Topology Design. These are primarily typical Combinat
 orial Optimization problems best known through their Mixed-Integer Program
 ming (MIP) formulations\, which were then cast into a corresponding QUBO f
 ormulation [6].\nWhile these problem classes vary in their individual prop
 erties\, such as objective and variable types\, coefficient ranges\, and d
 ensity\, they all become challenging for established classical methods at 
 system sizes of approximately 100 to 10\,000 decision variables. The small
  sizes at which difficult problem instances appear enable testing quantum 
 algorithms for these problems already today. This is not\, however\, a sta
 tic collection\, and the archive is expected to grow over time with contri
 butions. We envision the OR community being able to provide this database 
 with interesting and relevant instances and solutions to the challenging p
 roblems within it\, as it did\, for example\, in the composition of the li
 brary of Mixed-Integer and Continuous Nonlinear Programming Instances (MIN
 LPLib) [7].\nIn this talk\, we reference the results from state-of-the-art
  solvers for instances from all problem classes and demonstrate exemplary 
 baseline results obtained with quantum solvers for selected classes. The b
 aseline results illustrate our suggested benchmark reporting\, aiming for 
 comparability of the used methods\, reproducibility of the respective solu
 tions\, and trackability of algorithmic and hardware improvements. We enco
 urage the optimization community to explore the performance evaluation of 
 available classical or quantum algorithms and hardware with the benchmarki
 ng problem instances presented in this library.\nThis work was an effort o
 f the IBM Quantum Optimization Working Group and resulted in the paper “
 Quantum Optimization Benchmark Library The Intractable Decathlon\,” whic
 h has already been submitted to ArXiV but is still pending publication\, a
 s well as the Quantum Optimization Benchmark Library (QOBLIB) repository [
 8].
URL:https://www.imperial.ac.uk/events/209913/quantum-optimization-benchmark
 ing-library-the-intractable-decathlon/
DTSTART;TZID=Europe/London:20260615T160000
DTEND;TZID=Europe/London:20260615T170000
LOCATION:LT3\, ACE Extension\, South Kensington Campus\, Imperial College L
 ondon\, London\, SW7 2AZ\, United Kingdom
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