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Related Concept Videos

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.
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Observational Learning01:12

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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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The Quantum-Mechanical Model of an Atom02:45

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Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing...
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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Introduction to Learning01:18

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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First Law: Particles in Two-dimensional Equilibrium01:18

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Recall that a particle in equilibrium is one for which the external forces are balanced. Static equilibrium involves objects at rest, and dynamic equilibrium involves objects in motion without acceleration; but it is important to remember that these conditions are relative. For instance, an object may be at rest when viewed from one frame of reference, but that same object would appear to be in motion when viewed by someone moving at a constant velocity.
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Updated: May 12, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Toward Quantum Federated Learning.

Chao Ren, Rudai Yan, Huihui Zhu

    IEEE Transactions on Neural Networks and Learning Systems
    |May 6, 2025
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    Summary
    This summary is machine-generated.

    Quantum federated learning (QFL) combines quantum computing and federated learning to enhance data privacy and efficiency. This review provides a comprehensive guide to QFL principles, techniques, and applications.

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    Area of Science:

    • Interdisciplinary field merging quantum computing (QC) and federated learning (FL).

    Background:

    • Existing research lacks a comprehensive survey of quantum federated learning (QFL).
    • QFL aims to enhance privacy, security, and efficiency in machine learning using quantum technologies.

    Purpose of the Study:

    • To provide a holistic examination and comprehensive understanding of QFL.
    • To identify challenges, opportunities, and future research directions in QFL.

    Main Methods:

    • Review of current research in quantum federated learning.
    • Proposal of a unique taxonomy for QFL techniques based on characteristics and quantum methods employed.

    Main Results:

    • Discussion of the principles, techniques, and emerging applications of QFL.
    • Identification of challenges and opportunities in integrating QC and FL.

    Conclusions:

    • QFL is a rapidly evolving field with potential for significant breakthroughs.
    • This review serves as a foundational guide for researchers and practitioners in QFL.