- •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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It is BornÕs rule in the Hilbert space formalism.
It is important to point out that if state ψ is an eigenstate of operator a representing observable a, i.e., aψ = αψ, then the outcome of observable a equals α with probability one.
We point out that, for any Þxed quantum state ψ, each quantum observable a can be represented as a classical random variable (Sect. 2). In the discrete case the corresponding probability distribution is deÞned as
P(A) = |
Pψ (a = αm), |
|
αm A |
where Pψ (a = αm) is given by BornÕs rule.
Thus each concrete quantum measurement can be described by the classical probability model.
Problems (including deep interpretational issues) arise only when one tries to describe classically data collected for a few incompatible observables (Sect. 5).
By using the BornÕs rule (18 ) and the classical probabilistic deÞnition of average (Sect. 2), it is easy to see that the average value of the observable a in the state ψ (belonging to the domain of deÞnition of the corresponding operator a) is given by
a ψ = a ψ, ψ . |
(21) |
For example, for an observable represented by an operator with the purely discrete spectrum, we have
a ψ = |
αmPψ (a = αm) = αm Pma ψ 2 = a ψ, ψ . |
m |
m |
Postulate 5 (Time Evolution of Wave Function) Let H be the Hamiltonian of a quantum system, i.e., the Hermitian operator corresponding to the energy observable. The time evolution of the wave function ψ H is described by the Schrödinger equation
d |
|
i dt ψ(t) = Hψ(t) |
(22) |
with the initial condition ψ(0) = ψ.
5 Compatible and Incompatible Observables
Two observables a and b are called compatible if a measurement procedure for their joint measurement can be designed, i.e., a measurement of the vector observable d = (a, b). In such a case their joint probability distribution is well deÞned.
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62 A. Khrennikov
In the opposite case, i.e., when such a joint-measurement procedure does not exist, observables are called incompatible. The joint probability distribution of incompatible observables has no meaning.
In QM, compatible observables a and b are represented by commuting Hermitian
operators |
a and b, i.e., [a, b] = 0; consequently, |
incompatible |
observables |
a |
and b are |
represented by noncommuting operators, |
i.e., [a, b] |
= 0. Thus |
in |
the QM-formalism compatibilityÐincompatibility is represented as commutativityÐ noncommutativity.
Postulate 4a (BornÕs Rule for Joint Measurements) Let observables a and b be represented by Hermitian operators a and b with purely discrete spectrum. The probability to obtain the eigenvalues αm and βk in a joint measurement of a and b in a state ψ—the joint probability distribution—is given by
Pψ (a = αm, b = βk ) = PkbPma ψ 2 = Pma Pkbψ 2. |
(23) |
It is crucial that the spectral projectors of commuting operators commute, so the probability distribution does not depend on the order of the values of observables. This is a classical probability distribution (Sect. 2). Any pair of compatible observables a and b can be represented by random variables: for example, by using the joint probability distribution as the probability measure.
A family of compatible observables a1, . . . , an is represented by commuting
Hermitian operators a1, . . . , an, i.e., [ai , aj ] = 0 for all pairs i, j. The |
joint |
probability distribution is given by the natural generalization of rule (23): |
|
Pa1,...,an;ψ (α1m, . . . , αnk ) ≡ Pψ (a1 = α1m, . . . , an = αnk ) |
|
= Pkan . . . .Pma1 ψ 2 = · · · . = Pma1 . . . .Pkan ψ 2, |
(24) |
where all possible permutations of projectors can be considered.
Now we point to one distinguishing feature of compatibility of quantum observables which is commonly not emphasized. The relation of commutativity of operators is the pairwise relation, it does not involve say triples of operators. Thus, for joint measurability of a group of quantum observables a1, . . . , an, their pairwise joint measurability is sufÞcient. Thus if we are able to design measurement procedures for all possible pairs, then we are always able to design a jointmeasurement procedure for the whole group of quantum observables a1, . . . , an. This is the specialty of quantum observables. In particular, if there exist all pairwise joint probability distributions Pai ,aj ;ψ , then the joint probability Pa1,...,an;ψ is deÞned as well.
The BornÕs rule can be generalized to the quantum states represented by density operators (Sect. 9.1, formula (40)).
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5.1 Post-Measurement State From the Projection Postulate
The projection postulate is one of the most questionable and debatable postulates of QM. We present it in the separate section to distinguish it from other postulates of QM, Postulates 1Ð5, which are commonly accepted.
Consider pure state ψ and quantum observable (Hermitian operator) a representing some physical observable a. Suppose that a has nondegenerate spectrum; denote its eigenvalues by α1, .., αm, . . . and the corresponding eigenvectors by e1a , . . . , ema , . . . (here αi = αj , i = j.) This is an orthonormal basis in H. We expand the vector ψ with respect to this basis:
ψ = k1e1a + · · · + kmema + · · · , |
(25) |
where (kj ) are complex numbers such that
ψ 2 = |k1|2 + · · · + |km|2 + · · · = 1. |
(26) |
By using the terminology of linear algebra we say that the pure state ψ is a superposition of the pure states ej . The von Neumann projection postulate describes the post-measurement state and it can be formulated as follows:
Postulate 6VN (Projection Postulate, von Neumann) Measurement of observable
a resulting in output αi induces reduction of superposition (25) to the basis vector eia .
The procedure described by the projection postulate can be interpreted in the following way:
Superposition (25) reßects uncertainty in the results of measurements for an observable a. Before measurement a quantum system Òdoes not know how it will answer to the question a.Ó The BornÕs rule presents potentialities for different answers. Thus a quantum system in the superposition state ψ does not have propensity to any value of a as its objective property. After the measurement the superposition is reduced to the single term in the expansion (25) corresponding to the value of a obtained in the process of measurement.
Consider now an arbitrary quantum observable a with purely discrete spectrum, i.e., a = α1Pαa1 + · · · + αmPαam + · · · . The LŸders projection postulate describes the post-measurement state and it can be formulated as follows:
Postulate 6L (Projection Postulate, LŸders) Measurement of observable a resulting in output αm induces projection of state ψ on state
P a ψ
ψαm = αm . Pαam ψ
In contrast to the majority of books on quantum theory, we sharply distinguish the cases of quantum observables with nondegenerate and degenerate spectra. von