最佳信息传输和统一的衡量概率空间
1Department of Physics, Williams College, Williamstown, MA 01267, USA.
Entropy (Basel, Switzerland)
|June 28, 2023
概括
本研究探讨了量子力学中统一测量的基本意义. 结果表明,这种措施优化了信息传输,但实际的希尔伯特空间结构可能是自然实现的必要条件.
科学领域:
- 量子信息理论 量子信息理论
- 量子测量是一种量子测量.
- 量子力学的基础 量子力学的基础
背景情况:
- 量子系统是由希尔伯特空间中的状态描述的.
- 完整的直角测量将量子状态映射到概率分布.
- 在复杂的希尔伯特空间中对单位球的均分布在概率简单上产生了统一的测量.
研究的目的:
- 调查概率简数上统一测量的基本意义.
- 确定这种统一的测量方法是否是量子系统中信息传输的最佳方法.
- 探索量子力学的基础数学结构的含义.
主要方法:
- 在d维希尔伯特空间中从均状态中产生的概率分布的分析.
- 信息理论场景定义,以评估统一措施的最佳性.
- 希尔伯特空间结构 (复杂与真实) 的作用的理论研究.
主要成果:
- 概率简单的统一测量被确定为在特定场景中信息传输的最佳测量.
- 这种最佳性与希尔伯特空间的复杂性密切相关.
- 自然地实现这种优化可能需要一个潜在的真实-希尔伯特空间结构.
结论:
- 统一的测量在量子信息传输中具有基本意义.
- 复杂的希尔伯特空间结构对于实现这种最佳信息传输至关重要.
- 对真实希尔伯特空间结构的进一步研究可能会为量子基础提供新的见解.
关键词:
信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息传输信息量子重建的量子重建相关概念视频
Probability Distributions
7.3K
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...
7.3K
Probability in Statistics
13.5K
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...
13.5K
Uniform Distribution
5.1K
The uniform distribution is a continuous probability distribution of events with an equal probability of occurrence. This distribution is rectangular.
Two essential properties of this distribution are
Two essential properties of this distribution are
5.1K
Propagation of Uncertainty from Random Error
738
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...
738
Probability Laws
41.1K
Overview
41.1K
Probability Histograms
11.8K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.8K


