Non-Markovianity in Discrete-Time Open Quantum Random Walk on Arbitrary Graphs
- URL: http://arxiv.org/abs/2407.20888v1
- Date: Tue, 30 Jul 2024 15:01:57 GMT
- Title: Non-Markovianity in Discrete-Time Open Quantum Random Walk on Arbitrary Graphs
- Authors: Monika Rani, Supriyo Dutta, Subhashish Banerjee,
- Abstract summary: We present a new model of the Discrete-Time Open Quantum Walk (DTOQW) applicable to an arbitrary graph.
The dynamics is gauged by computing the coherence and fidelity at different time steps.
- Score: 2.867517731896504
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a new model of the Discrete-Time Open Quantum Walk (DTOQW) applicable to an arbitrary graph, thereby going beyond the case of quantum walks on regular graphs. The impact of noise is studied by constructing Kraus operators of different dimensions, making use of the Weyl operators. These Kraus operators are employed as the coin operators of the DTOQW. The walker dynamics is studied under the impact of non-Markovian amplitude damping, depolarizing and dephasing noise channels. In addition, the walk is implemented on arbitrary graphs, such as path graph, cycle graph, star graph, complete graph, and complete bipartite graph. The dynamics, due to the influence of noise, is gauged by computing the coherence and fidelity at different time steps. Further, the probability distribution, representing the availability of the quantum walker at different vertices of the graph, is computed at different time steps for the above noises.
Related papers
- Graph Signal Sampling for Inductive One-Bit Matrix Completion: a
Closed-form Solution [112.3443939502313]
We propose a unified graph signal sampling framework which enjoys the benefits of graph signal analysis and processing.
The key idea is to transform each user's ratings on the items to a function (signal) on the vertices of an item-item graph.
For the online setting, we develop a Bayesian extension, i.e., BGS-IMC which considers continuous random Gaussian noise in the graph Fourier domain.
arXiv Detail & Related papers (2023-02-08T08:17:43Z) - Discrete Quantum Walks on the Symmetric Group [0.0]
In quantum walks, the propagation is governed by quantum mechanical rules; generalizing random walks to the quantum setting.
In this paper we investigate the discrete time coined quantum walk (DTCQW) model using tools from non-commutative Fourier analysis.
Specifically, we are interested in characterizing the DTCQW on Cayley graphs generated by the symmetric group ($sym$) with appropriate generating sets.
arXiv Detail & Related papers (2022-03-28T23:48:08Z) - Key graph properties affecting transport efficiency of flip-flop Grover
percolated quantum walks [0.0]
We study quantum walks with the flip-flop shift operator and the Grover coin.
We show how the position of the source and sink together with the graph geometry and its modifications affect transport.
This gives us a deep insight into processes where elongation or addition of dead-end subgraphs may surprisingly result in enhanced transport.
arXiv Detail & Related papers (2022-02-19T11:55:21Z) - 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) - Non-separable Spatio-temporal Graph Kernels via SPDEs [90.62347738138594]
We provide graph kernels for principled-temporal modelling on graphs.
By providing novel tools for modelling on graphs, we outperform pre-existing graph kernels in real-world applications.
arXiv Detail & Related papers (2021-11-16T14:53:19Z) - Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised
Node Classification [59.06717774425588]
We propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field.
It contains explicit pairwise factors to model output-output relations and uses a GNN backbone to model input-output relations.
We conduct experiments on various datasets, which shows that our model can effectively improve the performance for semi-supervised node classification on graphs.
arXiv Detail & Related papers (2021-07-27T19:47:53Z) - Continuous-time quantum walks in the presence of a quadratic
perturbation [55.41644538483948]
We address the properties of continuous-time quantum walks with Hamiltonians of the form $mathcalH= L + lambda L2$.
We consider cycle, complete, and star graphs because paradigmatic models with low/high connectivity and/or symmetry.
arXiv Detail & Related papers (2020-05-13T14:53:36Z) - Analysis of Lackadaisical Quantum Walks [0.0]
The lackadaisical quantum walk is a quantum analogue of the lazy random walk obtained by adding a self-loop to each.
We analytically prove that lackadaisical quantum walks can find a unique marked.
vertebrae on any regular locally arc-transitive graph with constant success probability.
quadratically faster than the hitting time.
arXiv Detail & Related papers (2020-02-26T00:40:25Z) - Block-Approximated Exponential Random Graphs [77.4792558024487]
An important challenge in the field of exponential random graphs (ERGs) is the fitting of non-trivial ERGs on large graphs.
We propose an approximative framework to such non-trivial ERGs that result in dyadic independence (i.e., edge independent) distributions.
Our methods are scalable to sparse graphs consisting of millions of nodes.
arXiv Detail & Related papers (2020-02-14T11:42:16Z) - Discrete-Time Quantum Walks on Oriented Graphs [0.0]
We define discrete-time quantum walks on arbitrary oriented graphs.
We introduce a parameter, called alpha, that quantifies the amount of orientation.
arXiv Detail & Related papers (2020-01-13T01:42:42Z)
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.