Many-body quantum resources of graph states
- URL: http://arxiv.org/abs/2410.12487v1
- Date: Wed, 16 Oct 2024 12:05:19 GMT
- Title: Many-body quantum resources of graph states
- Authors: Marcin Płodzień, Maciej Lewenstein, Jan Chwedeńczuk,
- Abstract summary: Characterizing the non-classical correlations of a complex many-body system is an important part of quantum technologies.
We consider four topologies, namely the star graph states with edges, Tur'an graphs, $r$-ary tree graphs, and square grid cluster states.
We characterize many-body entanglement depth in graph states with up to $8$ qubits in $146$ classes non-equivalent under local transformations and graph isomorphisms.
- Score: 0.0
- License:
- Abstract: Characterizing the non-classical correlations of a complex many-body system is an important part of quantum technologies. A versatile tool for such a task is one that scales well with the size of the system and which can be both easily computed and measured. In this work we focus on graph states, that are promising platforms for quantum computation, simulation and metrology. We consider four topologies, namely the star graph states with edges, Tur\'an graphs, $r$-ary tree graphs, and square grid cluster states, and provide a method to characterise their quantum content: the many-body Bell correlations, non-separability and entanglement depth for an arbitrary number of qubits. We also relate the strength of these many-body correlations to the usefulness of graph states for quantum sensing. Finally, we characterize many-body entanglement depth in graph states with up to $8$ qubits in $146$ classes non-equivalent under local transformations and graph isomorphisms. The technique presented is simple and does not make any assumptions about the multi-qubit state, so it could find applications wherever precise knowledge of many-body quantum correlations is required.
Related papers
- Gaussian Entanglement Measure: Applications to Multipartite Entanglement
of Graph States and Bosonic Field Theory [50.24983453990065]
An entanglement measure based on the Fubini-Study metric has been recently introduced by Cocchiarella and co-workers.
We present the Gaussian Entanglement Measure (GEM), a generalization of geometric entanglement measure for multimode Gaussian states.
By providing a computable multipartite entanglement measure for systems with a large number of degrees of freedom, we show that our definition can be used to obtain insights into a free bosonic field theory.
arXiv Detail & Related papers (2024-01-31T15:50:50Z) - New Approaches to Complexity via Quantum Graphs [0.0]
We introduce and study the clique problem for quantum graphs.
inputs for our problems are presented as quantum channels induced by circuits.
We show that, by varying the collection of channels in the language, these give rise to complete problems for the classes $textsfNP$, $textsfMA$, $textsfQMA$, and $textsfQMA(2)$.
arXiv Detail & Related papers (2023-09-22T14:20:14Z) - Entanglement, quantum correlators and connectivity in graph states [0.0]
This work contributes to a deeper understanding of the entanglement and connectivity properties of graph states.
It offers valuable insights for quantum information processing and quantum computing applications.
arXiv Detail & Related papers (2023-08-15T10:42:07Z) - Engineering Graph States of Atomic Ensembles by Photon-Mediated
Entanglement [0.0]
We report on the generation of continuous-variable graph states of atomic spin ensembles.
The edges represent the entanglement structure, which we program by combining global photon-mediated interactions in an optical cavity with local spin rotations.
We further engineer a four-mode square graph state, highlighting the flexibility of our approach.
arXiv Detail & Related papers (2022-12-22T18:46:17Z) - QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional
Networks [124.7972093110732]
We propose quantum graph convolutional networks (QuanGCN), which learns the local message passing among nodes with the sequence of crossing-gate quantum operations.
To mitigate the inherent noises from modern quantum devices, we apply sparse constraint to sparsify the nodes' connections.
Our QuanGCN is functionally comparable or even superior than the classical algorithms on several benchmark graph datasets.
arXiv Detail & Related papers (2022-11-09T21:43:16Z) - From Quantum Graph Computing to Quantum Graph Learning: A Survey [86.8206129053725]
We first elaborate the correlations between quantum mechanics and graph theory to show that quantum computers are able to generate useful solutions.
For its practicability and wide-applicability, we give a brief review of typical graph learning techniques.
We give a snapshot of quantum graph learning where expectations serve as a catalyst for subsequent research.
arXiv Detail & Related papers (2022-02-19T02:56:47Z) - Scalable approach to many-body localization via quantum data [69.3939291118954]
Many-body localization is a notoriously difficult phenomenon from quantum many-body physics.
We propose a flexible neural network based learning approach that circumvents any computationally expensive step.
Our approach can be applied to large-scale quantum experiments to provide new insights into quantum many-body physics.
arXiv Detail & Related papers (2022-02-17T19:00:09Z) - Benchmarking Small-Scale Quantum Devices on Computing Graph Edit
Distance [52.77024349608834]
Graph Edit Distance (GED) measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical.
In this paper we present a comparative study of two quantum approaches to computing GED.
arXiv Detail & Related papers (2021-11-19T12:35:26Z) - Quantum machine learning of graph-structured data [0.38581147665516596]
We consider graph-structured quantum data and describe how to carry out its quantum machine learning via quantum neural networks.
We explain how to systematically exploit this additional graph structure to improve quantum learning algorithms.
arXiv Detail & Related papers (2021-03-19T14:39:19Z) - Quantum walk processes in quantum devices [55.41644538483948]
We study how to represent quantum walk on a graph as a quantum circuit.
Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
arXiv Detail & Related papers (2020-12-28T18:04:16Z)
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.