用于求解最小装箱问题的量子与量子启发优化
Quantum and quantum-inspired optimization for solving the minimum bin packing problem
AI总结：
本文将乏核燃料深地质处置罐装填问题建模为最小装箱问题并转化为二次无约束二值优化形式，通过数值比较量子与量子启发方法，验证了其在原子能工业实际问题中的可行性。
摘要：
量子计算设备被认为在解决困难的计算任务方面具有强大能力，尤其是组合优化问题。在本工作中，我们考虑一种特定类型的最小装箱问题，该问题可用于解决与原子能工业相关的深地质处置罐中乏核燃料装填问题。我们首先将上述问题重新定义为二次无约束二值优化形式。这种表示与现有的量子退火设备以及量子启发算法天然兼容。随后，我们给出了量子方法与量子启发方法数值比较的结果。我们的研究结果表明，利用量子与量子启发优化解决原子能工业中具有行业相关性的问题是可行的。
英文摘要：
Quantum computing devices are believed to be powerful in solving hard computational tasks, in particular, combinatorial optimization problems. In the present work, we consider a particular type of the minimum bin packing problem, which can be used for solving the problem of filling spent nuclear fuel in deep-repository canisters that is relevant for atomic energy industry. We first redefine the aforementioned problem it in terms of quadratic unconstrained binary optimization. Such a representation is natively compatible with existing quantum annealing devices as well as quantum-inspired algorithms. We then present the results of the numerical comparison of quantum and quantum-inspired methods. Results of our study indicate on the possibility to solve industry-relevant problems of atomic energy industry using quantum and quantum-inspired optimization.
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