Quantum visual computing is advancing rapidly. This paper presents a new
formulation for stereo matching with nonlinear regularizers and spatial
pyramids on quantum annealers as a maximum a posteriori inference problem that
minimizes the energy of a Markov Random Field. Our approach is hybrid (i.e.,
quantum-classical) and is compatible with modern D-Wave quantum annealers,
i.e., it includes a quadratic unconstrained binary optimization (QUBO)
objective. Previous quantum annealing techniques for stereo matching are
limited to using linear regularizers, and thus, they do not exploit the
fundamental advantages of the quantum computing paradigm in solving
combinatorial optimization problems. In contrast, our method utilizes the full
potential of quantum annealing for stereo matching, as nonlinear regularizers
create optimization problems which are NP-hard. On the Middlebury benchmark, we
achieve an improved root mean squared accuracy over the previous state of the
art in quantum stereo matching of 2% and 22.5% when using different solvers.

该研究以量子视觉计算为背景，引入了一种使用非线性正则化方法和空间金字塔在量子退火器上解决最大后验推断问题的新方法，形成了一个最小化马尔可夫随机场能量的问题。该方法是混合（即量子 - 经典）的，适用于现代 D-Wave 量子退火器，并结合了二次无约束二进制优化（QUBO）目标函数，在解决组合优化问题时充分利用了量子计算模型的优势。通过在 Middlebury 基准测试上应用不同求解器，我们实现了 2％和 22.5％两种不同精度的根均方准确度改进，刷新了量子立体匹配领域的最新进展。