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Related Concept Videos

Associative Learning01:27

Associative Learning

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...
Observational Learning01:12

Observational Learning

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 because...
The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

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 hydrogen spectra. Schrödinger...
Cognitive Learning01:21

Cognitive Learning

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.
Tolman introduced the idea that behavior is influenced by...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
Network Covalent Solids02:18

Network Covalent Solids

Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...

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Related Experiment Videos

Experimentally Validated Quantum-Secure Federated Learning over a Multi-user Quantum Network.

Zhi-Ping Liu1,2, Xiao-Yu Cao1,2, Hao-Wen Liu1,2

  • 1National Laboratory of Solid State Microstructures and School of Physics, Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China.

Research (Washington, D.C.)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Quantum federated learning (QFL) enhances data privacy using quantum networks. This new protocol, QuNetQFL, offers information-theoretic security and improves model accuracy on quantum and real-world datasets.

Related Experiment Videos

Area of Science:

  • Quantum computing
  • Artificial intelligence
  • Cybersecurity

Background:

  • Federated learning (FL) enables decentralized training but faces privacy risks in the quantum era.
  • Quantum federated learning (QFL) promises enhanced security and efficiency but lacks practical, experimentally validated protocols.
  • A need exists for quantum-secure methods to protect data privacy during distributed machine learning.

Purpose of the Study:

  • To present and experimentally validate QuNetQFL, a novel QFL protocol for quantum networks.
  • To demonstrate information-theoretic security in QFL through distributed quantum secret keys.
  • To assess the performance and scalability of QuNetQFL on diverse datasets and tasks.

Main Methods:

  • Implementation of QuNetQFL on a 4-client quantum network.
  • Masking local model updates with distributed quantum secret keys for secure aggregation.
  • Experimental validation using quantum and real-world datasets for classification and language tasks.
  • Large-scale simulations for scalability assessment in handwritten-digit recognition.

Main Results:

  • Experimental validation on a 4-client quantum network confirmed protocol functionality.
  • Improved global accuracy in classifying quantum datasets by adding a single quantum client.
  • Comparable and robust performance for sentiment analysis using federated fine-tuning of a hybrid model.
  • Scalability demonstrated to 200 clients for digit recognition with significant communication cost reduction.

Conclusions:

  • QuNetQFL provides a practical and experimentally validated approach to quantum-secure federated learning.
  • The protocol offers information-theoretic security, enhancing privacy in distributed quantum machine learning.
  • QuNetQFL represents a scalable solution for the emerging quantum internet, applicable to various AI tasks.