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.

翻译：量子视觉计算正快速发展。本文提出一种新方法，将非线性正则化与空间金字塔应用于量子退火器上的立体匹配问题，作为最大后验推理问题（即马尔可夫随机场的能量最小化）。我们的方法是混合式的（即量子-经典混合），兼容现代D-Wave量子退火器，并包含二次无约束二元优化目标。以往的立体匹配量子退火技术仅限于线性正则化，因此未能充分利用量子计算范式在解决组合优化问题中的根本优势。相比之下，本方法充分发挥了量子退火在立体匹配中的潜力，因为非线性正则会生成NP难优化问题。在Middlebury基准测试中，使用不同求解器时，我们的方法在均方根精度上相较于之前量子立体匹配最佳结果分别提升了2%和22.5%。