- •Preface
- •Contents
- •Contributors
- •Modeling Meaning Associated with Documental Entities: Introducing the Brussels Quantum Approach
- •1 Introduction
- •2 The Double-Slit Experiment
- •3 Interrogative Processes
- •4 Modeling the QWeb
- •5 Adding Context
- •6 Conclusion
- •Appendix 1: Interference Plus Context Effects
- •Appendix 2: Meaning Bond
- •References
- •1 Introduction
- •2 Bell Test in the Problem of Cognitive Semantic Information Retrieval
- •2.1 Bell Inequality and Its Interpretation
- •2.2 Bell Test in Semantic Retrieving
- •3 Results
- •References
- •1 Introduction
- •2 Basics of Quantum Probability Theory
- •3 Steps to Build an HSM Model
- •3.1 How to Determine the Compatibility Relations
- •3.2 How to Determine the Dimension
- •3.5 Compute the Choice Probabilities
- •3.6 Estimate Model Parameters, Compare and Test Models
- •4 Computer Programs
- •5 Concluding Comments
- •References
- •Basics of Quantum Theory for Quantum-Like Modeling Information Retrieval
- •1 Introduction
- •3 Quantum Mathematics
- •3.1 Hermitian Operators in Hilbert Space
- •3.2 Pure and Mixed States: Normalized Vectors and Density Operators
- •4 Quantum Mechanics: Postulates
- •5 Compatible and Incompatible Observables
- •5.1 Post-Measurement State From the Projection Postulate
- •6 Interpretations of Quantum Mechanics
- •6.1 Ensemble and Individual Interpretations
- •6.2 Information Interpretations
- •7 Quantum Conditional (Transition) Probability
- •9 Formula of Total Probability with the Interference Term
- •9.1 Växjö (Realist Ensemble Contextual) Interpretation of Quantum Mechanics
- •10 Quantum Logic
- •11 Space of Square Integrable Functions as a State Space
- •12 Operation of Tensor Product
- •14 Qubit
- •15 Entanglement
- •References
- •1 Introduction
- •2 Background
- •2.1 Distributional Hypothesis
- •2.2 A Brief History of Word Embedding
- •3 Applications of Word Embedding
- •3.1 Word-Level Applications
- •3.2 Sentence-Level Application
- •3.3 Sentence-Pair Level Application
- •3.4 Seq2seq Application
- •3.5 Evaluation
- •4 Reconsidering Word Embedding
- •4.1 Limitations
- •4.2 Trends
- •4.4 Towards Dynamic Word Embedding
- •5 Conclusion
- •References
- •1 Introduction
- •2 Motivating Example: Car Dealership
- •3 Modelling Elementary Data Types
- •3.1 Orthogonal Data Types
- •3.2 Non-orthogonal Data Types
- •4 Data Type Construction
- •5 Quantum-Based Data Type Constructors
- •5.1 Tuple Data Type Constructor
- •5.2 Set Data Type Constructor
- •6 Conclusion
- •References
- •Incorporating Weights into a Quantum-Logic-Based Query Language
- •1 Introduction
- •2 A Motivating Example
- •5 Logic-Based Weighting
- •6 Related Work
- •7 Conclusion
- •References
- •Searching for Information with Meet and Join Operators
- •1 Introduction
- •2 Background
- •2.1 Vector Spaces
- •2.2 Sets Versus Vector Spaces
- •2.3 The Boolean Model for IR
- •2.5 The Probabilistic Models
- •3 Meet and Join
- •4 Structures of a Query-by-Theme Language
- •4.1 Features and Terms
- •4.2 Themes
- •4.3 Document Ranking
- •4.4 Meet and Join Operators
- •5 Implementation of a Query-by-Theme Language
- •6 Related Work
- •7 Discussion and Future Work
- •References
- •Index
- •Preface
- •Organization
- •Contents
- •Fundamentals
- •Why Should We Use Quantum Theory?
- •1 Introduction
- •2 On the Human Science/Natural Science Issue
- •3 The Human Roots of Quantum Science
- •4 Qualitative Parallels Between Quantum Theory and the Human Sciences
- •5 Early Quantitative Applications of Quantum Theory to the Human Sciences
- •6 Epilogue
- •References
- •Quantum Cognition
- •1 Introduction
- •2 The Quantum Persuasion Approach
- •3 Experimental Design
- •3.1 Testing for Perspective Incompatibility
- •3.2 Quantum Persuasion
- •3.3 Predictions
- •4 Results
- •4.1 Descriptive Statistics
- •4.2 Data Analysis
- •4.3 Interpretation
- •5 Discussion and Concluding Remarks
- •References
- •1 Introduction
- •2 A Probabilistic Fusion Model of Trust
- •3 Contextuality
- •4 Experiment
- •4.1 Subjects
- •4.2 Design and Materials
- •4.3 Procedure
- •4.4 Results
- •4.5 Discussion
- •5 Summary and Conclusions
- •References
- •Probabilistic Programs for Investigating Contextuality in Human Information Processing
- •1 Introduction
- •2 A Framework for Determining Contextuality in Human Information Processing
- •3 Using Probabilistic Programs to Simulate Bell Scenario Experiments
- •References
- •1 Familiarity and Recollection, Verbatim and Gist
- •2 True Memory, False Memory, over Distributed Memory
- •3 The Hamiltonian Based QEM Model
- •4 Data and Prediction
- •5 Discussion
- •References
- •Decision-Making
- •1 Introduction
- •1.2 Two Stage Gambling Game
- •2 Quantum Probabilities and Waves
- •2.1 Intensity Waves
- •2.2 The Law of Balance and Probability Waves
- •2.3 Probability Waves
- •3 Law of Maximal Uncertainty
- •3.1 Principle of Entropy
- •3.2 Mirror Principle
- •4 Conclusion
- •References
- •1 Introduction
- •4 Quantum-Like Bayesian Networks
- •7.1 Results and Discussion
- •8 Conclusion
- •References
- •Cybernetics and AI
- •1 Introduction
- •2 Modeling of the Vehicle
- •2.1 Introduction to Braitenberg Vehicles
- •2.2 Quantum Approach for BV Decision Making
- •3 Topics in Eigenlogic
- •3.1 The Eigenlogic Operators
- •3.2 Incorporation of Fuzzy Logic
- •4 BV Quantum Robot Simulation Results
- •4.1 Simulation Environment
- •5 Quantum Wheel of Emotions
- •6 Discussion and Conclusion
- •7 Credits and Acknowledgements
- •References
- •1 Introduction
- •2.1 What Is Intelligence?
- •2.2 Human Intelligence and Quantum Cognition
- •2.3 In Search of the General Principles of Intelligence
- •3 Towards a Moral Test
- •4 Compositional Quantum Cognition
- •4.1 Categorical Compositional Model of Meaning
- •4.2 Proof of Concept: Compositional Quantum Cognition
- •5 Implementation of a Moral Test
- •5.2 Step II: A Toy Example, Moral Dilemmas and Context Effects
- •5.4 Step IV. Application for AI
- •6 Discussion and Conclusion
- •Appendix A: Example of a Moral Dilemma
- •References
- •Probability and Beyond
- •1 Introduction
- •2 The Theory of Density Hypercubes
- •2.1 Construction of the Theory
- •2.2 Component Symmetries
- •2.3 Normalisation and Causality
- •3 Decoherence and Hyper-decoherence
- •3.1 Decoherence to Classical Theory
- •4 Higher Order Interference
- •5 Conclusions
- •A Proofs
- •References
- •Information Retrieval
- •1 Introduction
- •2 Related Work
- •3 Quantum Entanglement and Bell Inequality
- •5 Experiment Settings
- •5.1 Dataset
- •5.3 Experimental Procedure
- •6 Results and Discussion
- •7 Conclusion
- •A Appendix
- •References
- •Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
- •1 Introduction
- •2 Quantifying Relevance Dimensions
- •3 Deriving a Bell Inequality for Documents
- •3.1 CHSH Inequality
- •3.2 CHSH Inequality for Documents Using the Trace Method
- •4 Experiment and Results
- •5 Conclusion and Future Work
- •A Appendix
- •References
- •Short Paper
- •An Update on Updating
- •References
- •Author Index
- •The Sure Thing principle, the Disjunction Effect and the Law of Total Probability
- •Material and methods
- •Experimental results.
- •Experiment 1
- •Experiment 2
- •More versus less risk averse participants
- •Theoretical analysis
- •Shared features of the theoretical models
- •The Markov model
- •The quantum-like model
- •Logistic model
- •Theoretical model performance
- •Model comparison for risk attitude partitioning.
- •Discussion
- •Authors contributions
- •Ethical clearance
- •Funding
- •Acknowledgements
- •References
- •Markov versus quantum dynamic models of belief change during evidence monitoring
- •Results
- •Model comparisons.
- •Discussion
- •Methods
- •Participants.
- •Task.
- •Procedure.
- •Mathematical Models.
- •Acknowledgements
- •New Developments for Value-based Decisions
- •Context Effects in Preferential Choice
- •Comparison of Model Mechanisms
- •Qualitative Empirical Comparisons
- •Quantitative Empirical Comparisons
- •Neural Mechanisms of Value Accumulation
- •Neuroimaging Studies of Context Effects and Attribute-Wise Decision Processes
- •Concluding Remarks
- •Acknowledgments
- •References
- •Comparison of Markov versus quantum dynamical models of human decision making
- •CONFLICT OF INTEREST
- •Endnotes
- •FURTHER READING
- •REFERENCES
suai.ru/our-contacts |
quantum machine learning |
Basics of Quantum Theory for Quantum-Like Modeling Information Retrieval |
59 |
Moreover, any operator satisfying (a)Ð(d) represents a pure state. Properties (a) and
(d) characterize orthogonal projectors, property (b) is their consequence. Property
(c) implies that the projector is one dimensional.
The next step in the development of QM was the extension of the class of quantum states, from pure states represented by one dimensional projectors to states represented by linear operators having the properties (a)Ð(c). Such operators are called density operators. (This nontrivial step of extension of the class of quantum states was based on the efforts of Landau and von Neumann.) The symbol D(H ) denotes the space of density operators in the complex Hilbert space H.
One typically distinguishes pure states, as represented by one dimensional projectors, and mixed states, the density operators which cannot be represented by one dimensional projectors. The terminology ÒmixedÓ has the following origin: any density operator can be represented as a ÒmixtureÓ of pure states (ψi ) :
ρ = |
pi Pψi , pi [0, 1], pi = 1. |
(15) |
i |
i |
|
(To simplify formulas, we shall not put the operator-label ÒhatÓ in the symbols denoting density operators, i.e., ρ ≡ ρ.) The state is pure if and only if such a mixture is trivial: all pi , besides one, equal zero. However, by operating with the terminology Òmixed stateÓ one has to take into account that the representation in the form (15) is not unique. The same mixed state can be presented as mixtures of different collections of pure states.
Any operator ρ satisfying (a)Ð(c) is diagonalizable (even in inÞnite-dimensional Hilbert space), i.e., in some orthonormal basis it is represented as a diagonal matrix, ρ = diag(pj ), where pj [0, 1], j pj = 1. Thus it can be represented in the form (15) with mutually orthogonal one dimensional projectors. The property (d) can be used to check whether a state is pure or not.
We point out that pure states are merely mathematical abstractions; in real experimental situations, it is possible to prepare only mixed states. The degree of purity is deÞned as
purity(ρ) = Trρ2.
Experimenters are satisÞed by getting this quantity near one.
4 Quantum Mechanics: Postulates
We state again that H denotes complex Hilbert space with the scalar product ·, · and the norm · corresponding to the scalar product.
Postulate 1 (The Mathematical Description of Quantum States) Quantum (pure) states (wave functions) are represented by normalized vectors ψ (i.e., ψ 2 =ψ, ψ = 1) of a complex Hilbert space H. Every normalized vector ψ H may
suai.ru/our-contacts |
quantum machine learning |
60 |
A. Khrennikov |
represent a quantum state. any complex number c, |c| state.4
If a vector ψ corresponding to a state is multiplied by = 1, the resulting vector will correspond to the same
The physical meaning of Òa quantum stateÓ is not deÞned by this postulate, see Sect. 6.1.
Postulate 2 (The Mathematical Description of Physical Observables) A physical observable a is represented by a Hermitian operator a in complex Hilbert space H. Different observables are represented by different operators.
Postulate 3 (Spectral) For a physical observable a which is represented by the Hermitian operator a we can predict (together with some probabilities) values λ
Spec(a).
We restrict our considerations by simplest Hermitian operators which are analogous to discrete random variables in classical probability theory. We recall that a Hermitian operator a has purely discrete spectrum if it can be represented as
a = α1Pαa1 + · · · + αmPαam + · · · , αm R, |
(16) |
where Pαam are orthogonal projection operators related to the orthonormal eigenvectors {ekma }k of a corresponding to the eigenvalues αm by
Pαam ψ = ψ, ekma ekma , ψ H. |
(17) |
k |
|
Here k labels the eigenvectors ekma which belong to the same eigenvalue αm of a.
Postulate 4 (BornÕs Rule) Let a physical observable a be represented by a Hermitian operator a with purely discrete spectrum. The probability Pψ (a = αm) to obtain the eigenvalue αm of a for measurement of a in a state ψ is given by
Pψ (a = αm) = Pma ψ 2. |
(18) |
If the operator a has nondegenerate (purely discrete) spectrum, then each αm is associated with one dimensional subspace. The latter can be Þxed by selecting any normalized vector, say ema . In this case orthogonal projectors act simply as
Pαam ψ = ψ, ema ema . |
(19) |
Formula (18) takes a very simple form
Pψ (a = αm) = | ψ, ema |2. |
(20) |
4Thus states are given by elements of the unit sphere of the Hilbert space H.