Adaptive Framework for Failure-Aware Protocols in Fusion-Based Graph-State Generation
- URL: http://arxiv.org/abs/2601.02087v1
- Date: Mon, 05 Jan 2026 13:13:39 GMT
- Title: Adaptive Framework for Failure-Aware Protocols in Fusion-Based Graph-State Generation
- Authors: Korbinian Staudacher, Bhilahari Jeevanesan, Tobias Guggemos,
- Abstract summary: We develop a framework to optimize the building process using graph theoretic characterizations of fusion networks.<n>We present graph state generation protocols for linear cluster resource states and Type-I/Type-II fusions which are adaptive to fusion failure.<n>Our strategies can reduce the fusion overhead by several orders of magnitude when compared to simple repeat until success protocols.
- Score: 1.7205106391379026
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We consider the generation of photonic graph states in a linear optics setting where sequential non-deterministic fusion measurements are used to build large graph states out of small linear clusters and develop a framework to optimize the building process using graph theoretic characterizations of fusion networks. We present graph state generation protocols for linear cluster resource states and Type-I/Type-II fusions which are adaptive to fusion failure, that is, they reuse leftover graph states in the remaining building process. To estimate hardware costs, we interpret our protocols as finite Markov processes. This viewpoint allows to cast the expected number of fusion measurements until success as a first passage problem. We then deploy a pipeline of polynomial algorithms to optimize arbitrary graph states, extract fusion networks and find beneficial orderings of fusions with the goal of lowering the corresponding mean first passage times. We evaluate our pipeline for different initial resource states and fusion mechanisms with varying success probabilities. Results show that our strategies can reduce the fusion overhead by several orders of magnitude when compared to simple repeat until success protocols, especially for realistic fusion success probabilities between 50-75 %.
Related papers
- Finding trail covers: near-optimal decompositions of graph states as linear fusion networks [1.7842332554022695]
We study three graph-theoretic problems which can be seen as generalisations of the Eulerian and Hamiltonian path problems.<n>These arise in photonic implementations of measurement-based quantum computing.<n>We propose new rewrite strategies for graph states that reduce the number of fusions required to build a given graph.
arXiv Detail & Related papers (2025-08-25T18:06:53Z) - A Scalable and Robust Compilation Framework for Emitter-Photonic Graph State [1.3624495460189865]
We study the GraphState-to-Circuit compilation problem in the context of the deterministic scheme.<n>We propose a novel compilation framework that partitions the target graph state into subgraphs, compiles them individually, and subsequently combines and schedules the circuits to maximize emitter resource utilization.
arXiv Detail & Related papers (2025-03-20T17:01:33Z) - Transforming graph states via Bell state measurements [0.0]
Graph states are key resources for measurement-based quantum computing.<n>We derive graph transformations for all types of rotated type-II fusion, showing that there are five different fusion success cases.<n>We give application examples of the derived graph transformation rules and show that they can be used to construct graph codes or simulate fusion networks.
arXiv Detail & Related papers (2024-05-03T18:16:25Z) - Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data.
The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
arXiv Detail & Related papers (2024-01-12T17:57:07Z) - Boson subtraction as an alternative to fusion gates for generating graph
states [0.0]
We propose an alternative approach to generate graph states based on the graph picture of linear quantum networks (LQG picture)
These subtraction schemes correspond to efficient heralded optical setups with single-photon sources and more flexible measurement elements than fusion gates.
arXiv Detail & Related papers (2023-06-27T02:14:17Z) - 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) - Graph-theoretical optimization of fusion-based graph state generation [0.0]
We present a graph-theoretical strategy to effectively optimize fusion-based generation of any given graph state, along with a Python package OptGraphState.
Our strategy comprises three stages: simplifying the target graph state, building a fusion network, and determining the order of fusions.
arXiv Detail & Related papers (2023-04-24T10:46:54Z) - You Only Transfer What You Share: Intersection-Induced Graph Transfer
Learning for Link Prediction [79.15394378571132]
We investigate a previously overlooked phenomenon: in many cases, a densely connected, complementary graph can be found for the original graph.
The denser graph may share nodes with the original graph, which offers a natural bridge for transferring selective, meaningful knowledge.
We identify this setting as Graph Intersection-induced Transfer Learning (GITL), which is motivated by practical applications in e-commerce or academic co-authorship predictions.
arXiv Detail & Related papers (2023-02-27T22:56:06Z) - EGRC-Net: Embedding-induced Graph Refinement Clustering Network [66.44293190793294]
We propose a novel graph clustering network called Embedding-Induced Graph Refinement Clustering Network (EGRC-Net)
EGRC-Net effectively utilizes the learned embedding to adaptively refine the initial graph and enhance the clustering performance.
Our proposed methods consistently outperform several state-of-the-art approaches.
arXiv Detail & Related papers (2022-11-19T09:08:43Z) - A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
We propose a robust framework for adversarial graph embedding, named AGE.
AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution.
Based on this framework, we propose three models to handle three types of graph data.
arXiv Detail & Related papers (2021-05-22T07:05:48Z) - Heuristic Semi-Supervised Learning for Graph Generation Inspired by
Electoral College [80.67842220664231]
We propose a novel pre-processing technique, namely ELectoral COllege (ELCO), which automatically expands new nodes and edges to refine the label similarity within a dense subgraph.
In all setups tested, our method boosts the average score of base models by a large margin of 4.7 points, as well as consistently outperforms the state-of-the-art.
arXiv Detail & Related papers (2020-06-10T14:48:48Z)
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.