In this paper, we focus on discrete-time random walks (DTRWs) on the path graph with reflecting walls. Also we consider the corresponding continuous-time random walks (CTRWs), discrete-time quantum walks (DTQWs) and continuous-time quantum walks (CTQWs). We give formulas for the return probabilities of these four walks (DTRWs, CTRWs, DTQWs, CTQWs) at the boundary point of the path graph and discuss their relationships. Furthermore, by considering the Ehrenfest model, we derive the scaling limit theorems for the return probabilities of both the CTRW and the CTQW.In this paper, we focus on discrete-time random walks (DTRWs) on the path graph with reflecting walls. Also we consider the corresponding continuous-time random walks (CTRWs), discrete-time quantum walks (DTQWs) and continuous-time quantum walks (CTQWs). We give formulas for the return probabilities of these four walks (DTRWs, CTRWs, DTQWs, CTQWs) at the boundary point of the path graph and discuss their relationships. Furthermore, by considering the Ehrenfest model, we derive the scaling limit theorems for the return probabilities of both the CTRW and the CTQW.

Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its ApplicationsOct 28, 20250 citations

Implementation of a continuous-time quantum walk on a sparse graph

Zhaoyang Chen, Guanzhong Li, Lvzhou Li

Continuous-time quantum walks (CTQWs) play a crucial role in quantum computing, especially for designing quantum algorithms. However, how to efficiently implement CTQWs is a challenging issue. In this paper, we study the implementation of CTQWs on sparse graphs, i.e., constructing efficient quantum circuits for implementing the unitary operator ${e}^{\ensuremath{-}iHt}$, where $H=\ensuremath{\gamma}A$ ($\ensuremath{\gamma}$ is a constant and $A$ corresponds to the adjacency matrix of a graph). Our result is, for a $d$ -sparse graph with $N$ vertices and evolution time $t$ , we can approximate ${e}^{\ensuremath{-}iHt}$ by a quantum circuit with gate complexity $({d}^{3}\ensuremath{\parallel}H\ensuremath{\parallel}tN{log}_{2}{(N))}^{1+o(1)}$, compared to the general Pauli decomposition, which scales like $(\ensuremath{\parallel}H\ensuremath{\parallel}t{N}^{4}{log}_{2}{(N))}^{1+o(1)}$. For sparse graphs, for instance, $d=O(1)$ , we obtain a noticeable improvement. Interestingly, our technique is related to graph decomposition. More specifically, we decompose the graph into a union of star graphs, and correspondingly, the Hamiltonian $H$ can be represented as the sum of some Hamiltonians ${H}_{j}$ , where each ${e}^{\ensuremath{-}i{H}_{j}t}$ is a CTQW on a star graph which can be implemented efficiently.Continuous-time quantum walks (CTQWs) play a crucial role in quantum computing, especially for designing quantum algorithms. However, how to efficiently implement CTQWs is a challenging issue. In this paper, we study the implementation of CTQWs on sparse graphs, i.e., constructing efficient quantum circuits for implementing the unitary operator ${e}^{\ensuremath{-}iHt}$, where $H=\ensuremath{\gamma}A$ ($\ensuremath{\gamma}$ is a constant and $A$ corresponds to the adjacency matrix of a graph). Our result is, for a $d$-sparse graph with $N$ vertices and evolution time $t$, we can approximate ${e}^{\ensuremath{-}iHt}$ by a quantum circuit with gate complexity $({d}^{3}\ensuremath{\parallel}H\ensuremath{\parallel}tN{log}_{2}{(N))}^{1+o(1)}$, compared to the general Pauli decomposition, which scales like $(\ensuremath{\parallel}H\ensuremath{\parallel}t{N}^{4}{log}_{2}{(N))}^{1+o(1)}$. For sparse graphs, for instance, $d=O(1)$, we obtain a noticeable improvement. Interestingly, our technique is related to graph decomposition. More specifically, we decompose the graph into a union of star graphs, and correspondingly, the Hamiltonian $H$ can be represented as the sum of some Hamiltonians ${H}_{j}$, where each ${e}^{\ensuremath{-}i{H}_{j}t}$ is a CTQW on a star graph which can be implemented efficiently.

arXiv:2408.10553Physical Review ANov 15, 20246 citations

A Continuous - Time Quantum Walk for Attributed Graphs Matching

Tewabe Chekole

Diverse facets Of the Theory of Quantum Walks on Graph are reviewed Till now .In specific, Quantum network routing, Quantum Walk Search Algorithm, Element distinctness associated to the eigenvalues of Graphs and the use of these relation /connection in the study of Quantum walks is furthermore described. Different Researchers had contribution and put their benchmark idea Pertaining with this research concept. I furthermore try to investigate recent Application of Quantum walks, In specific the problem pertained with Graph matching i.e Matching nodes(vertices) of the Graphs. In this research paper,I consider how Continuous-time quantum walk (CTQW) can be directed to Graph-matching problems. The matching problem is abstracted using weighted(attributed) Graphs that connects vertices's of one Graph to other and Try to compute the distance b/n those Graphs Node's Beside that finding the matched nodes and the Cost related to Matching. eventually measuring the distance between two Graphs which might have different size then by using k-nearest neighbor (k-NN) method try to classifying those graph based on closest training examples in the feature space.Diverse facets Of the Theory of Quantum Walks on Graph are reviewed Till now .In specific, Quantum network routing, Quantum Walk Search Algorithm, Element distinctness associated to the eigenvalues of Graphs and the use of these relation /connection in the study of Quantum walks is furthermore described. Different Researchers had contribution and put their benchmark idea Pertaining with this research concept. I furthermore try to investigate recent Application of Quantum walks, In specific the problem pertained with Graph matching i.e Matching nodes(vertices) of the Graphs. In this research paper,I consider how Continuous-time quantum walk (CTQW) can be directed to Graph-matching problems. The matching problem is abstracted using weighted(attributed) Graphs that connects vertices's of one Graph to other and Try to compute the distance b/n those Graphs Node's Beside that finding the matched nodes and the Cost related to Matching. eventually measuring the distance between two Graphs which might have different size then by using k-nearest neighbor (k-NN) method try to classifying those graph based on closest training examples in the feature space.

arXiv:1801.10339Jan 31, 20180 citations

Simplifying continuous-time quantum walks on dynamic graphs

Rebekah Herrman, Thomas G. Wong

arXiv:2106.06015Quantum Information ProcessingJan 19, 20229 citations

A

corresponds to the adjacency matrix of a graph). Our result is, for a

d

-sparse graph with

N

vertices and evolution time

t

, we can approximate ${e}^{\ensuremath{-}iHt}$ by a quantum circuit with gate complexity $({d}^{3}\ensuremath{\parallel}H\ensuremath{\parallel}tN{log}_{2}{(N))}^{1+o(1)}$, compared to the general Pauli decomposition, which scales like $(\ensuremath{\parallel}H\ensuremath{\parallel}t{N}^{4}{log}_{2}{(N))}^{1+o(1)}$. For sparse graphs, for instance,

d=O(1)

, we obtain a noticeable improvement. Interestingly, our technique is related to graph decomposition. More specifically, we decompose the graph into a union of star graphs, and correspondingly, the Hamiltonian

H

can be represented as the sum of some Hamiltonians

{H}_{j}

, where each ${e}^{\ensuremath{-}i{H}_{j}t}$ is a CTQW on a star graph which can be implemented efficiently.