- •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
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56 |
A. Khrennikov |
3 Quantum Mathematics
We present the basic mathematical structures of QM and couple them to quantum physics.
3.1 Hermitian Operators in Hilbert Space
We recall the deÞnition of a complex Hilbert space. Denote it by H. This is a complex linear space endowed with a scalar product (a positive-deÞnite nondegenerate Hermitian form) which is complete with respect to the norm corresponding to the scalar product, ·|· . The norm is deÞned as
φ = φ|φ .
In the Þnite-dimensional case the norm and, hence, completeness are of no use. Thus those who have no idea about functional analysis (but know essentials of linear algebra) can treat H simply as a Þnite-dimensional complex linear space with the
scalar product. |
its conjugate is denoted by z, here |
|||||||
For a complex number z = x + iy, x, y R, |
||||||||
|
2 |
= zz¯ = x |
2 |
+ y |
2 |
¯ |
||
z¯ = x − iy. The absolute value of z is given by |z| |
|
|
|
. |
For readerÕs convenience, we recall that the scalar product is a function from the Cartesian product H × H to the Þeld of complex numbers C, ψ1, ψ2 → ψ1|ψ2 , having the following properties:
1.Positive-deÞniteness: ψ|ψ ≥ 0 with ψ, ψ = 0 if and only if ψ = 0.
2.Conjugate symmetry: ψ1|ψ2 = ψ2|ψ1
3.Linearity with respect to the second argument2: φ|k1ψ1 + k2ψ2 = k1 φ|ψ1 + k2 φ|ψ2 , where k1, k2 are the complex numbers.
A reader who does not feel comfortable in the abstract framework of functional analysis can simply proceed with the Hilbert space H = Cn, where C is the set of complex numbers, and the scalar product
u|v = |
ui v¯i , u = (u1, . . . , un), v = (v1, . . . , vn). |
(11) |
|
i |
|
In this case the above properties of a scalar product can be easily derived from (11). Instead of linear operators, one can consider matrices.
2In mathematical texts one typically considers linearity with respect to the Þrst argument. Thus a mathematician has to pay attention to this difference.
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Basics of Quantum Theory for Quantum-Like Modeling Information Retrieval |
57 |
We also recall a few basic notions of theory of linear operators in complex Hilbert space. A map a : H → H is called a linear operator, if it maps linear combination of vectors into linear combination of their images:
a(λ1ψ1 + λ2ψ2) = λ1aψ1 + λ2aψ2,
where λj C, ψj H, j = 1, 2.
For a linear operator a the symbol a denotes its adjoint operator which is
deÞned by the equality |
|
aψ1|ψ2 = ψ1|a ψ2 . |
(12) |
Let us select in H some orthonormal basis (ei ), i.e., ei |ej = δij . By denoting the matrix elements of the operators a and a as aij and aij , respectively, we rewrite the deÞnition (12) in terms of the matrix elements:
aij = a¯j i .
A linear operator a is called Hermitian if it coincides with its adjoint operator:
a = a .
If an orthonormal basis in H is Þxed, (ei ), and a is represented by its matrix, A = (aij ), where aij = aei |ej , then it is Hermitian if and only if
a¯ij = aj i .
We remark that, for a Hermitian operator, all its eigenvalues are real. In fact, this was one of the main reasons to represent quantum observables by Hermitian operators. In the quantum formalism, the spectrum of a linear operator (the set of eigenvalues while we are in the Þnite-dimensional case) coincides with the set of possibly observable values (Sect. 4, Postulate 3). We also recall that eigenvectors of Hermitian operators corresponding to different eigenvalues are orthogonal. This property of Hermitian operators plays some role in justiÞcation of the projection postulate of QM, see Sect. 5.1.
A linear operator a is positive-semidefinite if, for any φ H,
aφ|φ ≥ 0.
This is equivalent to positive-semideÞniteness of its matrix.
For a linear operator a, its trace is deÞned as the sum of diagonal elements of its matrix in any orthonormal basis:
Tr a = aii = aei |ei ,
ii
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A. Khrennikov |
i.e., this quantity does not depend on a basis.
Let L be a subspace of H. The orthogonal projector P : H → L onto this subspace is a Hermitian, idempotent (i.e., coinciding with its square), and positivesemideÞnite operator3:
(a)P = P ;
(b)P 2 = P ;
(c)P ≥ 0.
Here (c) is a consequence of (a) and (b). Moreover, an arbitrary linear operator satisfying (a) and (b) is an orthogonal projectorÑonto the subspace P H.
3.2Pure and Mixed States: Normalized Vectors and Density Operators
Pure quantum states are represented by normalized vectors, ψ H : ψ = 1. Two colinear vectors,
ψ = λψ, λ C, |λ| = 1, |
(13) |
represent the same pure state. Thus, rigorously speaking, a pure state is an equivalence class of vectors having the unit norm: ψ ψ for vectors coupled by (13). The unit sphere of H is split into disjoint classesÑpure states. However, in concrete calculations one typically uses just concrete representatives of equivalent classes, i.e., one works with normalized vectors.
Each pure state can also be represented as the projection operator Pψ which projects H onto one dimensional subspace based on ψ. For a vector φ H,
Pψ φ = φ|ψ ψ. |
(14) |
The trace of the one dimensional projector Pψ equals 1:
Tr Pψ = ψ|ψ = 1.
We summarize the properties of the operator Pψ representing the pure state ψ. It is
(a)Hermitian,
(b)positive-semideÞnite,
(c)trace one,
(d)idempotent.
3To simplify formulas, we shall not put the operator-label ÒhatÓ in the symbols denoting projectors, i.e., P ≡ P .