Related Experiment Video
Updated: Sep 21, 2025

06:42
Generation and Coherent Control of Pulsed Quantum Frequency Combs
Published on: June 8, 2018
9.1K
Return Probability of Quantum and Correlated Random Walks.
Chusei Kiumi1, Norio Konno2, Shunya Tamura1
1Graduate School of Science and Engineering, Yokohama National University, Hodogaya, Yokohama 240-8501, Japan.
Entropy (Basel, Switzerland)
|May 28, 2022
Summary
This study analyzes return probabilities for quantum and correlated random walks on a 1D lattice. Both walk types exhibit return probabilities expressible via Legendre polynomials, with quantum walks also linked to elliptic integrals.
Area of Science:
- Quantum mechanics
- Statistical physics
- Probability theory
Background:
- Return probability is a fundamental concept in classical random walk analysis.
- Understanding return probabilities is crucial for characterizing the long-term behavior of random processes.
Purpose of the Study:
- To investigate the return probability of quantum random walks.
- To analyze the return probability of correlated random walks.
- To compare these probabilities on a one-dimensional integer lattice.
Main Methods:
- Employed the path counting method for analysis.
- Utilized mathematical expressions involving Legendre polynomials.
- Derived generating functions for quantum walk return probabilities.
Main Results:
- Demonstrated that return probabilities for both quantum and correlated random walks can be expressed using Legendre polynomials.
- Showed that the generating function for quantum walk return probability is related to elliptic integrals of the first and second kinds.
Conclusions:
- Established a novel connection between quantum/correlated random walks and special functions (Legendre polynomials, elliptic integrals).
- Provides a new analytical framework for studying quantum and correlated random walks on lattices.
Related Concept Videos
Random Variables
13.5K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.5K
Probability Laws
41.9K
Overview
41.9K
Probability in Statistics
14.8K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
14.8K
Probability Distributions
8.0K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
8.0K
Propagation of Uncertainty from Random Error
1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
Poisson Probability Distribution
8.5K
A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
The...
8.5K

