Computer Vision (CV) labelling algorithms play a pivotal role in the domain of low-level vision. For decades, it has been known that these problems can be elegantly formulated as discrete energy minimization problems derived from probabilistic graphical models (such as Markov Random Fields). Despite recent advances in inference algorithms (such as graph-cut and message-passing algorithms), the resulting energy minimization problems are generally viewed as intractable. The emergence of quantum computations, which offer the potential for faster solutions to certain problems than classical methods, has led to an increased interest in utilizing quantum properties to overcome intractable problems. Recently, there has also been a growing interest in Quantum Computer Vision (QCV), with the hope of providing a credible alternative or assistant to deep learning solutions in the field. This study investigates a new Quantum Annealing based inference algorithm for CV discrete energy minimization problems. Our contribution is focused on Stereo Matching as a significant CV labeling problem. As a proof of concept, we also use a hybrid quantum-classical solver provided by D-Wave System to compare our results with the best classical inference algorithms in the literature.

翻译：计算机视觉（CV）中的标注算法在低级视觉领域扮演关键角色。数十年来，这类问题已被优雅地表述为基于概率图模型（如马尔可夫随机场）的离散能量最小化问题。尽管推理算法（如图割和消息传递算法）近年取得了进展，但由此产生的能量最小化问题通常仍被视为计算棘手的。量子计算的出现因对某些问题具有超越经典方法的潜在求解速度，促使人们日益关注利用量子特性来攻克棘手问题。近期，量子计算机视觉（QCV）的研究兴趣也与日俱增，有望为该领域中的深度学习解决方案提供可靠的替代或辅助手段。本研究提出了一种基于量子退火的新型推理算法，用于解决CV中的离散能量最小化问题。我们的贡献聚焦于立体匹配这一重要的CV标注问题。作为概念验证，我们还使用了D-Wave系统提供的混合量子-经典求解器，将结果与文献中最优的经典推理算法进行了比较。