A Quantum Algorithm for the Sub-Graph Isomorphism Problem
- URL: http://arxiv.org/abs/2111.09732v2
- Date: Fri, 2 Sep 2022 08:51:50 GMT
- Title: A Quantum Algorithm for the Sub-Graph Isomorphism Problem
- Authors: Nicola Mariella and Andrea Simonetto
- Abstract summary: We propose a novel variational method for solving the sub-graph isomorphism problem on a gate-based quantum computer.
We show that the method can be applied to realistic sub-graph isomorphism problem instances in the medium term.
- Score: 3.04585143845864
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We propose a novel variational method for solving the sub-graph isomorphism
problem on a gate-based quantum computer. The method relies (1) on a new
representation of the adjacency matrices of the underlying graphs, which
requires a number of qubits that scales logarithmically with the number of
vertices of the graphs; and (2) on a new Ansatz that can efficiently probe the
permutation space. Simulations are then presented to showcase the approach on
graphs up to 16 vertices, whereas, given the logarithmic scaling, the approach
could be applied to realistic sub-graph isomorphism problem instances in the
medium term.
Related papers
- A Spectral Interpretation of Redundancy in a Graph Reservoir [51.40366905583043]
This work revisits the definition of the reservoir in the Multiresolution Reservoir Graph Neural Network (MRGNN)<n>It proposes a variant based on a Fairing algorithm originally introduced in the field of surface design in computer graphics.<n>The core contribution of the paper lies in the theoretical analysis of the algorithm from a random walks perspective.
arXiv Detail & Related papers (2025-07-17T10:02:57Z) - Unifying quantum spatial search, state transfer and uniform sampling on graphs: simple and exact [2.871419116565751]
This article presents a novel and succinct algorithmic framework via alternating quantum walks.
It unifies quantum spatial search, state transfer and uniform sampling on a large class of graphs.
The approach is easy to use since it has a succinct formalism that depends only on the depth of the Laplacian eigenvalue set of the graph.
arXiv Detail & Related papers (2024-07-01T06:09:19Z) - Discrete Graph Auto-Encoder [52.50288418639075]
We introduce a new framework named Discrete Graph Auto-Encoder (DGAE)
We first use a permutation-equivariant auto-encoder to convert graphs into sets of discrete latent node representations.
In the second step, we sort the sets of discrete latent representations and learn their distribution with a specifically designed auto-regressive model.
arXiv Detail & Related papers (2023-06-13T12:40:39Z) - Harnessing spectral representations for subgraph alignment [15.86857474914914]
We propose a spectral representation for maps that is compact, easy to compute, robust to topological changes, easy to plug into existing pipelines, and is especially effective for subgraph alignment problems.
We report for the first time a surprising phenomenon where the partiality arising in the subgraph alignment task is manifested as a special structure of the map coefficients.
arXiv Detail & Related papers (2022-05-30T09:03:28Z) - Learning Sparse Graphs via Majorization-Minimization for Smooth Node
Signals [8.140698535149042]
We propose an algorithm for learning a sparse weighted graph by estimating its adjacency matrix.
We show that the proposed algorithm converges faster, in terms of the average number of iterations, than several existing methods in the literature.
arXiv Detail & Related papers (2022-02-06T17:06:13Z) - On The Variational Perspectives To The Graph Isomorphism Problem [0.0]
This paper studies the Graph Isomorphism Problem from a variational algorithmic perspective.
This study presents the results of the algorithms and the variations that occur therein for graphs of four and five nodes.
arXiv Detail & Related papers (2021-11-18T17:43:21Z) - Casting graph isomorphism as a point set registration problem using a
simplex embedding and sampling [0.0]
A graph can be represented as a point set in enough dimensions using a simplex embedding and sampling.
The isomorphism of them corresponds to the existence of a perfect registration between the point set forms of the graphs.
The related idea of equivalence classes suggests that graph canonization may be an important tool in tackling graph isomorphism problem.
arXiv Detail & Related papers (2021-11-15T12:16:21Z) - Line Graph Neural Networks for Link Prediction [71.00689542259052]
We consider the graph link prediction task, which is a classic graph analytical problem with many real-world applications.
In this formalism, a link prediction problem is converted to a graph classification task.
We propose to seek a radically different and novel path by making use of the line graphs in graph theory.
In particular, each node in a line graph corresponds to a unique edge in the original graph. Therefore, link prediction problems in the original graph can be equivalently solved as a node classification problem in its corresponding line graph, instead of a graph classification task.
arXiv Detail & Related papers (2020-10-20T05:54:31Z) - Online Dense Subgraph Discovery via Blurred-Graph Feedback [87.9850024070244]
We introduce a novel learning problem for dense subgraph discovery.
We first propose a edge-time algorithm that obtains a nearly-optimal solution with high probability.
We then design a more scalable algorithm with a theoretical guarantee.
arXiv Detail & Related papers (2020-06-24T11:37:33Z) - Offline detection of change-points in the mean for stationary graph
signals [55.98760097296213]
We propose an offline method that relies on the concept of graph signal stationarity.
Our detector comes with a proof of a non-asymptotic inequality oracle.
arXiv Detail & Related papers (2020-06-18T15:51:38Z) - Wasserstein-based Graph Alignment [56.84964475441094]
We cast a new formulation for the one-to-many graph alignment problem, which aims at matching a node in the smaller graph with one or more nodes in the larger graph.
We show that our method leads to significant improvements with respect to the state-of-the-art algorithms for each of these tasks.
arXiv Detail & Related papers (2020-03-12T22:31:59Z) - Permutation Invariant Graph Generation via Score-Based Generative
Modeling [114.12935776726606]
We propose a permutation invariant approach to modeling graphs, using the recent framework of score-based generative modeling.
In particular, we design a permutation equivariant, multi-channel graph neural network to model the gradient of the data distribution at the input graph.
For graph generation, we find that our learning approach achieves better or comparable results to existing models on benchmark datasets.
arXiv Detail & Related papers (2020-03-02T03:06:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.