Results of Quantagonia's Hybrid Solver and D-Wave's LeapBQMHybrid Solver on the QOPTLib Optimization Benchmark

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This dataset contains the instances and results presented in the following paper: Eneko Osaba, Esther Villar-Rodriguez, Aitor Gomez-Tejedor, and Izaskun Oregi. Hybrid Quantum Solvers in Production: how to succeed in the NISQ era? arXiv preprint arXiv:2401.10302 (2024). Abstract: Hybrid quantum computing is considered the present and the future within the field of quantum computing. Far from being a passing fad, this trend cannot be considered just a stopgap to address the limitations of NISQ-era devices. The foundations linking both computing paradigms will remain robust over time. Despite buoyant research activity, the challenges in hybrid computing are still countless, ranging from the proper characterization of current solvers to the establishment of appropriate methodologies for the design and fair evaluation of hybrid algorithms. The contribution of this work is twofold: first, we describe and categorize some of the most frequently used hybrid solvers, resorting to two different taxonomies recently published in the literature. Secondly, we put a special focus on two solvers that are currently deployed in real production and that have demonstrated to be near the real industry. These solvers are the LeapHybridBQMSampler contained in D-Wave's Hybrid Solver Service and Quantagonia's Hybrid Solver. We analyze the performance of both hybrid methods using as benchmarks four well-known combinatorial optimization problems: the Traveling Salesman Problem, Vehicle Routing Problem, Bin Packing Problem, and Maximum Cut Problem. Thanks to the contributions presented in this paper, the reader gains insight into the performance of those hybridization strategies nowadays in production and close to the industrial markets.

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