TY - GEN
T1 - Hybrid Quantum Solvers in Production
T2 - 25th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2024
AU - Osaba, Eneko
AU - Villar-Rodríguez, Esther
AU - Gomez-Tejedor, Aitor
AU - Oregi, Izaskun
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - 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. 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 methods using as benchmarks four combinatorial optimization problems.
AB - 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. 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 methods using as benchmarks four combinatorial optimization problems.
KW - Combinatorial Optimization
KW - D-Wave
KW - Hybrid Quantum-Classical Computing
KW - Quantagonia
KW - Quantum Computing
UR - http://www.scopus.com/inward/record.url?scp=85210849617&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-77738-7_35
DO - 10.1007/978-3-031-77738-7_35
M3 - Conference contribution
AN - SCOPUS:85210849617
SN - 9783031777370
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 423
EP - 434
BT - Intelligent Data Engineering and Automated Learning – IDEAL 2024 - 25th International Conference, Proceedings
A2 - Julian, Vicente
A2 - Camacho, David
A2 - Yin, Hujun
A2 - Alberola, Juan M.
A2 - Nogueira, Vitor Beires
A2 - Novais, Paulo
A2 - Tallón-Ballesteros, Antonio
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 20 November 2024 through 22 November 2024
ER -