35,887 research outputs found
FPT-Algorithms for Computing Gromov-Hausdorff and Interleaving Distances Between Trees
The Gromov-Hausdorff distance is a natural way to measure the distortion between two metric spaces. However, there has been only limited algorithmic development to compute or approximate this distance. We focus on computing the Gromov-Hausdorff distance between two metric trees. Roughly speaking, a metric tree is a metric space that can be realized by the shortest path metric on a tree. Any finite tree with positive edge weight can be viewed as a metric tree where the weight is treated as edge length and the metric is the induced shortest path metric in the tree. Previously, Agarwal et al. showed that even for trees with unit edge length, it is NP-hard to approximate the Gromov-Hausdorff distance between them within a factor of 3. In this paper, we present a fixed-parameter tractable (FPT) algorithm that can approximate the Gromov-Hausdorff distance between two general metric trees within a multiplicative factor of 14.
Interestingly, the development of our algorithm is made possible by a connection between the Gromov-Hausdorff distance for metric trees and the interleaving distance for the so-called merge trees. The merge trees arise in practice naturally as a simple yet meaningful topological summary (it is a variant of the Reeb graphs and contour trees), and are of independent interest. It turns out that an exact or approximation algorithm for the interleaving distance leads to an approximation algorithm for the Gromov-Hausdorff distance. One of the key contributions of our work is that we re-define the interleaving distance in a way that makes it easier to develop dynamic programming approaches to compute it. We then present a fixed-parameter tractable algorithm to compute the interleaving distance between two merge trees exactly, which ultimately leads to an FPT-algorithm to approximate the Gromov-Hausdorff distance between two metric trees. This exact FPT-algorithm to compute the interleaving distance between merge trees is of interest itself, as it is known that it is NP-hard to approximate it within a factor of 3, and previously the best known algorithm has an approximation factor of O(sqrt{n}) even for trees with unit edge length
Auto-dual connected operators based on iterative merging algorithms
This paper proposes a new set of connected operators that are autodual. Classical connected operators are analyzed within the framework of merging algorithms. The discussion highlights three basic notions: merging order , merging criterion and region model. As a result a general merging algorithm is proposed. It can be used to create new connected operators and in particular autodual operators. Implementation issues are also discussed.Peer ReviewedPostprint (published version
Representing and retrieving regions using binary partition trees
This paper discusses the interest of Binary Partition Trees for image and region representation in the context of indexing and similarity based retrieval. Binary Partition Trees concentrate in a compact and structured way the set of regions that compose an image. Since the tree is able to represent images in a multiresolution way, only simple descriptors need to be attached to the nodes. Moreover, this representation is used for similarity based region retrieval.Peer ReviewedPostprint (published version
Surface code quantum computing by lattice surgery
In recent years, surface codes have become a leading method for quantum error
correction in theoretical large scale computational and communications
architecture designs. Their comparatively high fault-tolerant thresholds and
their natural 2-dimensional nearest neighbour (2DNN) structure make them an
obvious choice for large scale designs in experimentally realistic systems.
While fundamentally based on the toric code of Kitaev, there are many variants,
two of which are the planar- and defect- based codes. Planar codes require
fewer qubits to implement (for the same strength of error correction), but are
restricted to encoding a single qubit of information. Interactions between
encoded qubits are achieved via transversal operations, thus destroying the
inherent 2DNN nature of the code. In this paper we introduce a new technique
enabling the coupling of two planar codes without transversal operations,
maintaining the 2DNN of the encoded computer. Our lattice surgery technique
comprises splitting and merging planar code surfaces, and enables us to perform
universal quantum computation (including magic state injection) while removing
the need for braided logic in a strictly 2DNN design, and hence reduces the
overall qubit resources for logic operations. Those resources are further
reduced by the use of a rotated lattice for the planar encoding. We show how
lattice surgery allows us to distribute encoded GHZ states in a more direct
(and overhead friendly) manner, and how a demonstration of an encoded CNOT
between two distance 3 logical states is possible with 53 physical qubits, half
of that required in any other known construction in 2D.Comment: Published version. 29 pages, 18 figure
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