Motion segmentation is a challenging problem that seeks to identify
independent motions in two or several input images. This paper introduces the
first algorithm for motion segmentation that relies on adiabatic quantum
optimization of the objective function. The proposed method achieves on-par
performance with the state of the art on problem instances which can be mapped
to modern quantum annealers.

本文介绍了一种基于绝热量子优化的运动分割算法，它在可映射到现代量子退火器的问题实例上达到了与现有技术相当的性能。

量子运动分割

Quantum Motion Segmentation

In [Choi08], we introduced the notion of minor-embedding in adiabatic quantum
optimization. A minor-embedding of a graph G in a quantum hardware graph U is a
subgraph of U such that G can be obtained from it by contracting edges. In this
paper, we describe the intertwined adiabatic quantum architecture design
problem, which is to construct a hardware graph U that satisfies all known
physical constraints and, at the same time, permits an efficient
minor-embedding algorithm. We illustrate an optimal complete-graph-minor
hardware graph. Given a family F of graphs, a (host) graph U is called
F-minor-universal if for each graph G in F, U contains a minor-embedding of G.
The problem for designing a F-minor-universal hardware graph U_{sparse} in
which F consists of a family of sparse graphs (e.g., bounded degree graphs) is
open.

本文提出了交织的量子绝热架构设计问题，要求构建一个满足所有已知物理约束的硬件图 U，同时允许一种高效的 minor-embedding 算法，最后给出了一个最佳的完全图 minor。