从随机序列中对门组属性的影子估计
J Helsen1,2, M Ioannou3, J Kitzinger3,4
1QuSoft, Centrum Wiskunde & Informatica (CWI), Amsterdam, The Netherlands. jonas1helsen@gmail.com.
Nature communications
|August 19, 2023
概括
这项研究引入了随机序列估计,一种用于表征量子门集的新方法. 这种方法通过将复杂的分析转移到经典的后处理来简化实验,从而实现精确的量子诊断.
科学领域:
- 量子信息科学 量子信息科学
- 量子计算硬件 量子计算硬件
背景情况:
- 量子计算设备的规模和复杂性在不断增长.
- 对于量子运算越来越需要精确的诊断工具.
- 当前的量子设备在门序列长度和测量类型方面存在局限性.
研究的目的:
- 开发一种用于表征量子门集的新范式.
- 克服当前用于诊断的量子设备的局限性.
- 为量子信息处理提供一个多功能实验框架.
主要方法:
- 引入随机序列估计 (RSE) 作为一种新的实验技术.
- 开发了强大的影子估计通道变体.
- 利用经典的后处理来处理实验的复杂性.
主要成果:
- 从一个单一的实验中,RSE可以实现大量的估计问题.
- 在部分,压缩和全过程断层扫描中展示了应用.
- 能够有效地学习保利噪声和诊断量子门交叉通话.
结论:
- RSE提供了一种强大而灵活的方法来进行量子门组的表征.
- 该方法适用于优化量子门和诊断设备错误.
- 这种范式转变简化了实验要求,同时提高了诊断能力.
相关概念视频
Wald-Wolfowitz Runs Test II
267
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
267
Wald-Wolfowitz Runs Test I
676
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
The test works...
676
Random Variables
12.4K
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...
12.4K
Random Error
924
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
924
Unusual Results
3.2K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
3.2K
Random Sampling Method
11.2K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.2K


