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相关概念视频

Reinforcement01:23

Reinforcement

274
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

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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...
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Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

554
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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通过强化学习进行近似的自主量子错误纠正.

Yexiong Zeng1,2, Zheng-Yang Zhou1, Enrico Rinaldi1,2,3,4,5

  • 1Theoretical Quantum Physics Laboratory, Cluster for Pioneering Research, RIKEN, Wakoshi, Saitama 351-0198, Japan.

Physical review letters
|August 18, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的玻色子代码,用于使用强化学习进行近似自主量子错误校正 (AQEC). 新的RL代码有效地抑制了光子损失,简化了对强大的量子计算的实验实施.

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科学领域:

  • 量子信息科学 量子信息科学
  • 量子计算是一种量子计算.
  • 量子错误纠正方法 量子错误纠正方法

背景情况:

  • 自主量子错误校正 (AQEC) 提供了一种保护逻辑量子比特的途径,而不需要测量反循环.
  • 玻色代码由于其可控性,对AQEC具有前景,但现有的方法需要复杂的实现 (哈密尔顿距离d≥2).

研究的目的:

  • 提出和验证一个玻色子代码,用于近似的AQEC,缓解严格的Knill-Laflamme条件.
  • 为了减少在玻色子系统中实施AQEC的实验复杂性.

主要方法:

  • 利用强化学习 (RL) 来识别最佳的玻色代码单词集.
  • 开发了一个基于简化的哈密尔顿距离 (d=1) 的附属系统的纠错哈密尔顿式.

主要成果:

  • 识别了一个由Fock状态二和四组成的RL代码.
  • 证明有效地抑制单光子损失,将其减少到可管理的脱相过程.
  • 与以前的方法相比,实现了模型复杂性的显著降低.

结论:

  • 拟议的近似AQEC玻色子代码为量子错误抑制提供了一个实用的方法.
  • 这种基于d=1哈密尔顿距离的简化方法是可扩展量子计算机的一个有希望的构建块.