- •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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by von Neumann [60] for individual randomness! For him Òstatistical interpretationÓ had the meaning which is totally different from the BallentineÕs Òensemblestatistical interpretation.Ó J. von Neumann wanted to emphasize the difference between deterministic (Newtonian) classical mechanics in that the state of a system is determined by values of two observables (position and momentum) and quantum mechanics in that the state is determined not by values of observables, but by probabilities. We shall follow Albert Einstein and use the terminology ensemble interpretation.
We remark that following von Neumann [60] the supporters of the individual interpretation believe in irreducible quantum randomness, i.e., that the behavior of an individual quantum system is irreducibly random. Why does it behave in such a way? Because it is quantum, so it can behave so unusually. Nowadays this von NeumannÕs claim is used to justify superiority of the quantum technology over the classical technology. For example, superiority of quantum random generators.
6.2 Information Interpretations
The quantum information revolution generated a variety of information interpretations of QM (see, for example, [16, 17, 19, 20]). By these interpretations the quantum formalism describes special way of information processing, more general than the classical information processing. Roughly speaking one can forget about physics and work solely with probability, entropy, and information. Quantum Bayesianism (QBism) [25, 26] can be considered as one of such information, in its extreme form: the quantum formalism describes very general scheme of assignments of subjective probabilities to possible outcomes of experiments, assignment by human agents.
7 Quantum Conditional (Transition) Probability
In the classical Kolmogorov probabilistic model (Sect. 2), besides probabilities one operates with the conditional probabilities deÞned by the Bayes formula (see Sect. 2, formula (9)). The BornÕs postulate deÞning quantum probability should also be completed by a deÞnition of the conditional probability. We have remarked that, for one concrete observable, the probability given by BornÕs rule can be treated classically. However, the deÞnition of the conditional probability involves two observables. Such situations cannot be treated classically (at least straightforwardly, cf. Sect. 2). Thus conditional probability is really a quantum probability.
Let physical observables a and b be represented by Hermitian operators with purely discrete (may be degenerate) spectra:
a = αmPαam , b = βmPβbm . |
(27) |
m |
m |
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66 A. Khrennikov
Let ψ be a pure state and let Pαak ψ = 0. Then the probability to get the value b = βm under the condition that the value a = αk was observed in the preceding measurement of the observable a on the state ψ is given by the formula
|
b |
a |
ψ |
2 |
|
|
||
Pψ (b = βm|a = αk ) ≡ |
Pβm Pαk |
|
. |
(28) |
||||
|
P a |
ψ |
|
2 |
||||
|
αk |
|
|
|
|
|
One can motivate this deÞnition by appealing to version). After the a-measurement with output a projected onto the state
P a ψ
ψαk = αk
Pαak ψ
the projection postulate (LŸdersÕ = αk initially prepared state ψ is
.
Then one applies BornÕs rule to the b-measurement for this state.
Let the operator a has nondegenerate spectrum, i.e., for any eigenvalue α the corresponding eigenspace (i.e., generated by eigenvectors with aψ = αψ) is one dimensional. We can write
Pψ (b = βm|a = αk ) = Pβbm eka 2 |
(29) |
(here aeka = αk eka ). Thus the conditional probability in this case does not depend on the original state ψ. We can say that the memory about the original state was
destroyed. If also the operator b has nondegenerate spectrum, then we have: Pψ (b =
βm|a = αk ) = | emb , eka |2 and Pψ (a = αk |b = βm) = | eka , emb |2. By using symmetry of the scalar product we obtain the following result:
Let both operators a and b have purely discrete nondegenerate spectra and let
Pka ψ = 0 and Pmb ψ = 0. Then conditional probability is symmetric and it does not |
|
depend on the original state ψ : |
|
Pψ (b = βm|a = αk ) = Pψ (a = αk |b = βm) = | emb , eka |2. |
(30) |
8Observables with Nondegenerate Spectra: Double-Stochasticity of the Matrix of Transition Probabilities
We remark that classical (KolmogorovÐBayes) conditional probability is not symmetric, besides very special situations. Thus QM is described by a very specific probabilistic model.
Consider two nondegenerate observables. Set pβ|α = P(b = β|a = α). The matrix of transition probabilities Pb|a = (pβ|α ) is not only stochastic, i.e.,
pβ|α = 1
β