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：量子视觉计算正在迅速发展。本文提出了一种基于量子退火器的立体匹配新框架，将非线性正则化与空间金字塔结构结合，构建为最小化马尔可夫随机场能量的最大后验推理问题。我们的方法采用混合（即量子-经典）架构，兼容现代D-Wave量子退火器，其目标函数可表述为二次无约束二值优化（QUBO）问题。现有量子退火立体匹配技术仅局限于线性正则化器，未能充分发挥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，本方法通过非线性正则化器构建NP难优化问题，充分释放了量子退火在立体匹配中的潜力。在Middlebury基准测试中，采用不同求解器时，我们分别实现了比现有量子立体匹配最佳方法提升2%和22.5%的均方根精度。