- •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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but it is even doubly stochastic:
pβ|α = | eβb , eαa |2 = eβb , eβb = 1.
αα
In KolmogorovÕs model, stochasticity is the general property of matrices of transition probabilities. However, in general classical matrices of transition probabilities are not doubly stochastic. Hence, double stochasticity is a very speciÞc property of quantum probability.
We remark that statistical data collected outside quantum physics, e.g., in decision making by humans and psychology, violates the quantum law of double stochasticity [38]. Such data cannot be mathematically represented with the aid of Hermitian operators with nondegenerate spectra. One has to consider either Hermitian operators with degenerate spectra or positive operator valued measures (POVMs).
9 Formula of Total Probability with the Interference Term
We shall show that the quantum probabilistic calculus violates the conventional FTP (10), one of the basic laws of classical probability theory. In this section, we proceed in the abstract setting by operating with two abstract incompatible observables. The concrete realization of this setting for the two-slit experiment demonstrating interference of probabilities in QM will be presented in Sect. 16 which is closely related to FeynmanÕs claim [22, 23] on the nonclassical probabilistic structure of this experiment.
Let H2 = C×C be the two dimensional complex Hilbert space and let ψ H2 be a quantum state. Let us consider two dichotomous observables b = β1, β2 and a = α1, α2 represented by Hermitian operators b and a, respectively (one may consider simply Hermitian matrices). Let eb = {eβb } and ea = {eαa } be two orthonormal bases consisting of eigenvectors of the operators. The state ψ can be represented in the two ways
ψ = c1e1a + c2e2a , cα = ψ, eαa ; |
(31) |
ψ = d1e1b + d2e2b, dβ = ψ, eβb . |
(32) |
By Postulate 4 we have |
|
P(a = α) ≡ Pψ (a = α) = |cα |2. |
(33) |
P(b = β) ≡ Pψ (b = β) = |dβ |2. |
(34) |
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A. Khrennikov |
The possibility to expand one basis with respect to another basis induces connection between the probabilities P(a = α) and P(b = β). Let us expand the vectors eαa with respect to the basis eb
e1a = u11e1b + u12e2b |
(35) |
e2a = u21e1b + u22e2b, |
(36) |
where uαβ = eαa , eβb . Thus d1 = c1u11 + c2u21, d2 = c1u12 + c1u22. We obtain the quantum rule for transformation of probabilities:
P(b = β) = |c1u1β + c2u2β |2. |
(37) |
On the other hand, by the deÞnition of quantum conditional probability, see (28), we obtain
P(b = β|a = α) ≡ Pψ (b = β|a = α) = | eαa , eβb |2. |
(38) |
By combining (33), (34) and (37), (38) we obtain the quantum formula of total probabilityÑthe formula of the interference of probabilities:
P(b = β) = P(a = α)P(b = β|a = α) |
(39) |
α
+2 cos θ P(a = α1)P(b = β|a = α1)P(a = α2)P(b = β|a = α2).
In general cos θ = 0. Thus the quantum FTP does not coincide with the classical FTP (10) which is based on the BayesÕ formula.
We presented the derivation of the quantum FTP only for observables given by Hermitian operators acting in the two dimensional Hilbert space and for pure states. In Sect. 9.1, we give (without proving) the formula for spaces of an arbitrary dimension and states represented by density operators (see [42] for quantum FTP for observables represented by POVMs).
9.1Växjö (Realist Ensemble Contextual) Interpretation of Quantum Mechanics
The VŠxjš interpretation [33] is the realist ensemble contextual interpretation of QM. Thus, in contrast to Copenhagenists or QBists, by the VŠxjš interpretation QM is not complete and it can be emergent from a subquantum model. This interpretation is the ensemble interpretation This interpretation is contextual, i.e., experimental contexts have to be taken into account really seriously.
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By the VŠxjš interpretation the probabilistic part of QM is a special mathematical formalism to work with contextual probabilities for families of contexts, which are, in general, incompatible. Of course, the quantum probabilistic formalism is not the only possible formalism to operate with contextual probabilities.
The main distinguishing feature of the formalism of quantum probability is its complex Hilbert space representation and the BornÕs rule. All quantum contexts can be unified with the aid of a quantum state ψ (wave function, complex probability amplitude). A state represents only a part of context, the second part is given by an observable a. Thus the quantum probability model is not just a collection of Kolmogorov probability spaces. These spaces are coupled by quantum states.
Each theory of probability has two main purposes: descriptive and predictive. In classical probability theory its predictive machinery is based on Bayesian inference and, in particular, FTP (Sect. 2, formula (10)).
Can the probabilistic formalism of QM be treated as a generalization of Bayesian inference?
My viewpoint is that the quantum FTP with the interference term (Sect. 9, formula (39)) is, in fact, a modiÞed rule for the probability update. QM provides the following inference machinery. There are given a mixed state represented by density operator ρ and two quantum observables a and b represented mathematically by Hermitian operators a and b with purely discrete spectra. The Þrst measurement of a can be treated as collection of information about the state ρ. The result a = αi appears with the probability
pa (αi ) = TrPia ρ. |
(40) |
This is generalization of the BornÕs rule to mixed states.
Postulate 6L (the projection postulate in the LŸdersÕ form) can be extended to mixed states. Initial state ρ is transferred to the state
|
P a ρP a |
|
ρai = |
i i |
(41) |
TrPia ρPia . |
Then, for each state ρai , we perform measurement of b and obtain probabilities
p(βj |αi ) = TrPjbρai . |
(42) |
These are quantum conditional (transition) probabilities for the initial state given by a density operator (generalization of the formalism of Sect. 7).
We now recall the general form of the quantum FTP [42]:
p(b = β) = p(b = β|a = αk )p(a = αk ) |
(43) |
k
+2 cos φj ;k,m p(b = β|a = αk )p(a = αk )p(b = β|αm)p(a = αm).
k<m