论文标题
培训QUBO任务的神经网络的见解
Insights on Training Neural Networks for QUBO Tasks
论文作者
论文摘要
当前的硬件限制通过量子近似优化算法（QAOA）或量子退火（QA）求解二次无约束的二进制优化（QUBO）问题时限制了潜力。因此，我们考虑在这种情况下培训神经网络。我们首先讨论源自旅行推销员问题的翻译实例（TSP）的QUBO问题：通过自动编码器分析此表示形式表明，求解原始TSP所需的信息还要多。然后，我们表明神经网络可用于从QUBO输入和自动编码器的HiddenState表示中求解TSP实例。我们最终概括了该方法，并成功地训练神经网络以解决任意的QUBO问题，草图意味着将神经形态硬件用作模拟器或其他用于量子计算的协调员。
Current hardware limitations restrict the potential when solving quadratic unconstrained binary optimization (QUBO) problems via the quantum approximate optimization algorithm (QAOA) or quantum annealing (QA). Thus, we consider training neural networks in this context. We first discuss QUBO problems that originate from translated instances of the traveling salesman problem (TSP): Analyzing this representation via autoencoders shows that there is way more information included than necessary to solve the original TSP. Then we show that neural networks can be used to solve TSP instances from both QUBO input and autoencoders' hiddenstate representation. We finally generalize the approach and successfully train neural networks to solve arbitrary QUBO problems, sketching means to use neuromorphic hardware as a simulator or an additional co-processor for quantum computing.
