Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Quantum-Mechanical Model of an Atom02:45

The Quantum-Mechanical Model of an Atom

55.1K
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.
55.1K
Expected Value01:15

Expected Value

6.8K
The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:
6.8K
Language and Cognition01:27

Language and Cognition

570
Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
570
Quantum Numbers02:43

Quantum Numbers

47.4K
It is said that the energy of an electron in an atom is quantized; that is, it can be equal only to certain specific values and can jump from one energy level to another but not transition smoothly or stay between these levels.
47.4K
Components of Language01:24

Components of Language

596
Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
596
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

330
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
330

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

GNSS/SINS/DVL integrated navigation algorithm based on adaptive differential Kalman filtering.

PloS one·2026
Same author

The correction of thermodynamic data published in Journal of Hazardous Materials 461 (2024) 132464.

Journal of hazardous materials·2025
Same author

Stimulated Raman Scattering Microscopy Facilitates the Discovery of Diacylglycerol <i>O</i>-Acyltransferase 2 as a Target to Enhance Iodine Uptake in Papillary Thyroid Carcinoma.

Analytical chemistry·2025
Same author

Determination Method of Optimal Decomposition Level of Discrete Wavelet Based on Joint Jarque-Bera Test and Combination Weighting Method.

Entropy (Basel, Switzerland)·2025
Same author

Gel-Based Electrolytes for Organic Electrochemical Transistors: Mechanisms, Applications, and Perspectives.

Small (Weinheim an der Bergstrasse, Germany)·2025
Same author

Immune-Related Genes Associated with Interstitial Lung Disease in Dermatomyositis.

International journal of general medicine·2024
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 27, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

854

A Quantum Expectation Value Based Language Model with Application to Question Answering.

Qin Zhao1, Chenguang Hou2, Changjian Liu1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a Quantum Expectation Value based Language Model (QEV-LM) for information retrieval. It efficiently computes question-answer similarity using a shared density matrix and quantum expectation values, achieving excellent performance.

Keywords:
density matrixinterpretabilityobservablequantum expectation valuequantum language model

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

948

Related Experiment Videos

Last Updated: Nov 27, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

854
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

948

Area of Science:

  • Natural Language Processing
  • Quantum Computing
  • Information Retrieval

Background:

  • Quantum-inspired language models offer transparency and interpretability in Information Retrieval.
  • Existing models focus on subspaces, neglecting the semantic Hilbert space's overall density matrix.
  • A novel approach is needed to explore the full semantic Hilbert space for language modeling.

Purpose of the Study:

  • To propose a novel Quantum Expectation Value based Language Model (QEV-LM).
  • To construct a unique shared density matrix for the entire Semantic Hilbert Space.
  • To represent words and sentences as observables and similarity as quantum expectation values.

Main Methods:

  • Developed a Quantum Expectation Value based Language Model (QEV-LM).
  • Constructed a shared density matrix for the Semantic Hilbert Space.
  • Defined words and sentences as observables within the quantum model.
  • Interpreted question-answer similarity as the quantum expectation value of a joint observable.

Main Results:

  • The QEV-LM model demonstrated theoretical soundness.
  • Experiments on TREC-QA and WIKIQA datasets showed excellent performance.
  • The model exhibited significant computational efficiency with low time consumption.

Conclusions:

  • The proposed QEV-LM offers a novel quantum approach to language modeling in Information Retrieval.
  • Utilizing the entire Semantic Hilbert Space's density matrix enhances model capabilities.
  • The model achieves a balance of high performance and computational efficiency.