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arXiv 2409.04477quant-phcond-mat.mes-hall
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用于高阶二进制优化的偏置场数字化反绝热量子算法

Bias-Field Digitized Counterdiabatic Quantum Algorithm for Higher-Order Binary Optimization

Sebastián V. Romero, Anne-Maria Visuri, Alejandro Gomez Cadavid, Anton Simen, Enrique Solano, Narendra N. Hegade

AI总结:

针对高阶无约束二进制优化问题,提出偏置场数字化反绝热量子优化算法BF-DCQO,在IBM量子处理器实验验证中性能优于多种标准方法,还证明其在更大规模量子处理器及MAX 3-SAT问题上的有效性。

摘要:

组合优化在许多工业应用中起着至关重要的作用。虽然经典计算在处理复杂实例时常常面临困难,但量子优化已成为一种很有前景的替代方案。在此,我们提出一种增强的偏置场数字化反绝热量子优化(BF-DCQO)算法,以解决高阶无约束二进制优化(HUBO)问题。我们将BF-DCQO应用于伊辛自旋玻璃模型中具有三局部项的HUBO问题,并在IBM量子处理器上使用156个量子比特进行了实验验证。在研究的实例中,我们的结果优于量子近似优化算法、量子退火、模拟退火和禁忌搜索等标准方法。此外,我们提供了数值证据,证明在433量子比特的类Osprey量子处理器上解决类似HUBO问题的可行性。最后,我们在IonQ模拟器中解决了更密集的MAX 3-SAT问题实例。我们的结果表明,BF-DCQO为在当前和近期量子处理器上解决大规模HUBO问题提供了有效途径。

英文摘要:

Combinatorial optimization plays a crucial role in many industrial applications. While classical computing often struggles with complex instances, quantum optimization emerges as a promising alternative. Here, we present an enhanced bias-field digitized counterdiabatic quantum optimization (BF-DCQO) algorithm to address higher-order unconstrained binary optimization (HUBO). We apply BF-DCQO to a HUBO problem featuring three-local terms in the Ising spin-glass model, validated experimentally using 156 qubits on an IBM quantum processor. In the studied instances, our results outperform standard methods such as the quantum approximate optimization algorithm, quantum annealing, simulated annealing, and Tabu search. Furthermore, we provide numerical evidence of the feasibility of a similar HUBO problem on a 433-qubit Osprey-like quantum processor. Finally, we solve denser instances of the MAX 3-SAT problem in an IonQ emulator. Our results show that BF-DCQO offers an effective path for solving large-scale HUBO problems on current and near-term quantum processors.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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