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.

中文翻译：

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用于解决最小装箱问题的量子和量子启发优化

量子计算设备被认为在解决困难计算任务方面非常强大，特别是组合优化问题。在目前的工作中，我们考虑一种特殊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层储存罐中乏核燃料的填充问题。我们首先根据二次无约束二元优化重新定义上述问题。这种表示与现有的量子退火设备以及量子启发算法本身兼容。然后我们展示了量子方法和量子启发方法的数值比较结果。我们的研究结果表明使用量子和量子启发的优化来解决原子能工业的工业相关问题的可能性。