Modern adiabatic quantum computers (AQC) are already used to solve difficult combinatorial optimisation problems in various domains of science. Currently, only a few applications of AQC in computer vision have been demonstrated. We review AQC and derive a new algorithm for correspondence problems on point sets suitable for execution on AQC. Our algorithm has a subquadratic computational complexity of the state preparation. Examples of successful transformation estimation and point set alignment by simulated sampling are shown in the systematic experimental evaluation. Finally, we analyse the differences in the solutions and the corresponding energy values.

中文翻译：

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点集对应问题的量子计算方法

现代绝热量子计算机 (AQC) 已被用于解决各个科学领域中困难的组合优化问题。目前，只有少数 AQC 在计算机视觉中的应用得到了证明。我们回顾了 AQC 并推导出一种新算法，用于解决适合在 AQC 上执行的点集上的对应问题。我们的算法具有状态准备的次二次计算复杂度。系统实验评估中显示了通过模拟采样成功转换估计和点集对齐的示例。最后，我们分析了解决方案和相应能量值的差异。