Benchmarking Classical Solutions for Quantum Projects

⚛️ Quantum Portfolio Optimization: An Extensive Benchmark 📑 Recently, several researchers proposed portfolio optimization as a potential use case for quantum optimization. However, the literature is lacking an extensive benchmark quantifying the potential of quantum computers for portfolio optimization. In this work, we fill this gap. We provide a computational study, comparing quantum approaches against state-of-the-art classical methods on a meaningful, real-world instance set. In particular, we compare quantum annealing and the quantum approximate optimization algorithm against classical mixed-integer programming, simulated annealing, steepest descent local search, tabu search and a problem-tailored heuristics. We consider a variant of portfolio optimization which we show to be particular difficult for classical solvers in practice. Our benchmark comprises 250 instances with up to 1,000 assets from actual stock data. The results show that all instances can be solved to proven optimality by mixed-integer programming in the order of seconds. Moreover, the problem-tailored heuristic consistently outperforms quantum approaches in terms of solution quality for fixed runtime. Thus, we conclude that there is only very limited room for a potential quantum advantage in portfolio optimization. ℹ️ Eric Stopfer and Friedrich Wagner - Fraunhofer Institute for Integrated Circuits, Nürnberg, Germany - 2025

Quantum Optimization Benchmarking Library: The Intractable Decathlon https://lnkd.in/eGDJMccM Abstract Through recent progress in hardware development, quantum computers have advanced to the point where benchmarking of (heuristic) quantum algorithms at scale is within reach. Particularly in combinatorial optimization–where most algorithms are heuristics–it is key to empirically analyze their performance on hardware and track progress towards quantum advantage. To this extent, we present ten optimization problem classes that are difficult for existing classical algorithms and can (mostly) be linked to practically relevant applications, with the goal to enable systematic, fair, and comparable benchmarks for quantum optimization methods. Further, we introduce the Quantum Optimization Benchmarking Library [QOBLIB] where the problem instances and solution track records can be found. The individual properties of the problem classes vary in terms of objective and variable type, coefficient ranges, and density. Crucially, they all become challenging for established classical methods already at system sizes ranging from less than 100 to, at most, an order of 100 000 decision variables, allowing to approach them with today’s quantum computers. 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 problems. The baseline results illustrate a standardized form to present benchmarking solutions, which has been designed to ensure comparability of the used methods, reproducibility of the respective results, and trackability of algorithmic and hardware improvements over time. We encourage the optimization community to explore the performance of available classical or quantum algorithms and hardware platforms with the benchmarking problem instances presented in this work toward demonstrating quantum advantage in optimization.

🚀 Super excited to share our latest paper from the BlueQubit team! We’ve just published a new method and open-source SDK for Pauli Path Simulation (PPS) — a hardware-agnostic, scalable, and transparent classical simulator capable of modeling utility-scale (50+ qubit) quantum circuits, including IBM’s 127-qubit kicked Ising model from their 2023 Nature paper. 🧠 PPS sits at the intersection of quantum hardware and classical algorithmic innovation. Unlike other simulation methods that require hardware-specific tuning, PPS is general-purpose — making it a valuable tool for: 📍 Benchmarking and validating quantum experiments 📍 Guiding the next wave of quantum advantage claims 📍 Establishing rigorous classical baselines for quantum utility ⚛️ PPS empowers researchers to explore the true boundary where classical simulation ends and quantum advantage begins. We believe tools like this are essential for building trust and confidence in the progress of quantum computing. 🔍 Curious about how PPS works or how to get started? Check out our paper 🔗 https://lnkd.in/dGj9DaN5 #QuantumComputing #QuantumAdvantage #PauliPathSimulation #BlueQubit #QuantumResearch #HighPerformanceComputing #OpenSource