Generating graph states with a single quantum emitter and the minimum number of fusions
- URL: http://arxiv.org/abs/2412.04587v2
- Date: Sun, 19 Jan 2025 21:10:06 GMT
- Title: Generating graph states with a single quantum emitter and the minimum number of fusions
- Authors: Matthias C. Löbl, Love A. Pettersson, Andrew Jena, Luca Dellantonio, Stefano Paesani, Anders S. Sørensen,
- Abstract summary: Graph states are key resources for measurement- and fusion-based quantum computing with photons.
We optimize a hybrid graph-state generation scheme using a single quantum emitter and linear optics Bell-state measurements.
We present construction protocols of selected graph states and provide the lookup table.
- Score: 0.24739484546803336
- License:
- Abstract: Graph states are the key resources for measurement- and fusion-based quantum computing with photons, yet their creation is experimentally challenging. We optimize a hybrid graph-state generation scheme using a single quantum emitter and linear optics Bell-state measurements, called fusions. We first generate a restricted class of states from a single quantum emitter and then apply fusions to create a target graph state, where we use a dynamic programming approach to find the construction that requires the lowest possible number of fusions. Our analysis yields a lookup table for constructing $\sim 2.8\times 10^7$ non-isomorphic graph states with the minimum number of fusions. The lookup table covers all graph states with up to eight qubits and several other ones with up to 14 qubits. We present construction protocols of selected graph states and provide the lookup table. For large graph states that are not in the lookup table, we derive bounds for the required number of fusions using graph-theoretic properties. Finally, we use the lookup table to search for the best graph codes for loss-tolerant encodings, given a fixed number of fusions for their construction.
Related papers
- InstructG2I: Synthesizing Images from Multimodal Attributed Graphs [50.852150521561676]
We propose a graph context-conditioned diffusion model called InstructG2I.
InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling.
A Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process.
arXiv Detail & Related papers (2024-10-09T17:56:15Z) - Transforming graph states via Bell state measurements [0.0]
Graph states are key resources for measurement-based quantum computing.
Fusions are probabilistic Bell state measurements measuring pairs of parity operators of two qubits.
We derive graph transformations for all fusion types showing that there are five different types of fusion success cases.
arXiv Detail & Related papers (2024-05-03T18:16:25Z) - Fusion of deterministically generated photonic graph states [0.0]
Entanglement has evolved from an enigmatic concept of quantum physics to a key ingredient of quantum technology.
Here we achieve this goal by employing an optical resonator containing two atoms.
arXiv Detail & Related papers (2024-03-18T16:46:00Z) - Sampling and Uniqueness Sets in Graphon Signal Processing [136.68956350251418]
We study the properties of sampling sets on families of large graphs by leveraging the theory of graphons and graph limits.
We exploit the convergence results to provide an algorithm that obtains approximately close to optimal sampling sets.
arXiv Detail & Related papers (2024-01-11T22:31:48Z) - Generating scalable graph states in an atom-nanophotonic interface [0.0]
scalable graph states are essential for measurement-based quantum computation and many entanglement-assisted applications in quantum technologies.
Here we propose to prepare high-fidelity and scalable graph states in one and two dimensions, which can be tailored in an atom-nanophotonic cavity.
An analysis of state fidelity is also presented, and the state preparation probability can be optimized via multiqubit state carvings and sequential single-photon probes.
arXiv Detail & Related papers (2023-10-06T03:33:32Z) - 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 Mixup with Soft Alignments [49.61520432554505]
We study graph data augmentation by mixup, which has been used successfully on images.
We propose S-Mixup, a simple yet effective mixup method for graph classification by soft alignments.
arXiv Detail & Related papers (2023-06-11T22:04:28Z) - Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN.
We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
arXiv Detail & Related papers (2023-05-18T07:27:18Z) - 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) - 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)
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.