- •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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A. Khrennikov |
Thus we can predict the probability of the result βj for the b-observable on the basis of the probabilities for the results αi for the a-observable and conditional probabilities. Of course, the main nonclassical feature of this probability update rule is the presence of phase angles. In the case of dichotomous observables of the von NeumannÐLŸders type the phase angles φj can be expressed in terms of probabilities.
10 Quantum Logic
von Neumann and Birkhoff [11, 61] suggested to represent events (propositions) by orthogonal projectors in complex Hilbert space H.
For an orthogonal projector P , we set HP = P (H ), its image, and vice versa, for subspace L of H, the corresponding orthogonal projector is denoted by the symbol
PL.
The set of orthogonal projectors is a lattice with the order structure: P ≤ Q iff HP HQ or equivalently, for any ψ H, ψ|P ψ ≤ ψ|Qψ .
We recall that the lattice of projectors is endowed with operations ÒandÓ ( ) and ÒorÓ ( ). For two projectors P1, P2, the projector R = P1 P2 is deÞned as the projector onto the subspace HR and the projector S = P1 P2 is deÞned as the projector onto the subspace HR deÞned as the minimal linear subspace containing the set-theoretic union HP1 HP2 of subspaces HP1 , HP2 : this is the space of all linear combinations of vectors belonging to these subspaces. The operation of negation is deÞned as the orthogonal complement: P = {y H :y|x = 0 for all x HP }.
In the language of subspaces the operation ÒandÓ coincides with the usual settheoretic intersection, but the operations ÒorÓ and ÒnotÓ are nontrivial deformations of the corresponding set-theoretic operations. It is natural to expect that such deformations can induce deviations from classical Boolean logic.
Consider the following simple example. Let H be two dimensional Hilbert space |
|||
√ |
|
|
= 0 |
with the orthonormal basis (e1, e2) and let v = (e1 + e2)/ 2. Then Pv Pe1 |
and Pv Pe2 = 0, but Pv (Pe1 Pe2 ) = Pv . Hence, for quantum events, in general the distributivity law is violated:
P (P1 P2) = (P P1) (P P2). |
(44) |
The lattice of orthogonal projectors is called quantum logic. It is considered as a (very special) generalization of classical Boolean logic. Any sub-lattice consisting of commuting projectors can be treated as classical Boolean logic.
At the Þrst sight the representation of events by projectors/linear subspaces might look exotic. However, this is simply a prejudice which springs from too common usage of the set-theoretic representation of events (Boolean logic) in the modern classical probability theory. The tradition to represent events by subsets was Þrmly established by A. N. Kolmogorov in 1933. We remark that before him the basic
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classical probabilistic models were not of the set-theoretic nature. For example, the main competitor of the Kolmogorov model, the von Mises frequency model, was based on the notion of a collective.
As we have seen, quantum logic relaxes some constraints posed on the operations of classical Boolean logic, in particular, the distributivity constraint. This provides novel possibilities for logically consistent reasoning.
Since human decision makers violate FTP [32, 38]Ñthe basic law of classical probability, it seems that they process information by using nonclassical logic. Quantum logic is one of the possible candidates for logic of human reasoning. However, one has to remember that in principle other types of nonclassical logic may be useful for mathematical modeling of human decision making.
11 Space of Square Integrable Functions as a State Space
Although we generally proceed with Þnite-dimensional Hilbert spaces, it is useful to mention the most important example of inÞnite-dimensional Hilbert space used in QM. Consider the space of complex valued functions, ψ : Rm → C, which are square integrable with respect to the Lebesgue measure on Rm :
ψ 2 = |ψ(x)|2dx < ∞. |
(45) |
Rm
It is denoted by the symbol L2(Rm). Here the scalar product is given by
ψ1|ψ2 = |
|
ψ |
|
|
Rm |
¯ 1 |
(x)ψ2 |
(x)dx. |
A delicate point is that, for some measurable functions, ψ : Rm → C, which are not identically zero, the integral
|ψ(x)|2dx = 0. |
(46) |
Rm
We remark that the latter equality implies that ψ(x) = 0 a.e. (almost everywhere). Thus the quantity deÞned by (45) is, in fact, not norm: ψ = 0 does not imply that ψ = 0. To deÞne a proper Hilbert space, one has to consider as its elements not simply functions, but classes of equivalent functions, where the equivalence relation is deÞned as ψ φ if and only if ψ(x) = φ(x) a.e. In particular, all functions satisfying (46) are equivalent to the zero-function.
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72 A. Khrennikov
12 Operation of Tensor Product
Let both state spaces be L2-spaces, the spaces of complex valued square integrable functions: H1 = L2(Rk ) and L2(Rm).
Take two functions: ψ ≡ ψ(x) belongs to H1 and φ ≡ φ(y) belongs to H2. By multiplying these functions we obtain the function of two variables(x, y) = ψ(x) × φ(y), where × denotes the usual point wise product.5 It is easy to check that this function belongs to the space H = L2(Rk+m). Take now n functions, ψ1(x), . . . , ψn(x), from H1 and n functions, φ1(y), . . . φn(y), from H2 and consider the sum of their pairwise products:
(x, y) = ψi (x) × φi (y). |
(47) |
i |
|
This function also belongs to H.
It is possible to show that any function belonging to H can be represented as (47), where the sum is in general inÞnite. Multiplication of functions is the basic example of the operation of the tensor product. The latter is denoted by the symbol . Thus in the example under consideration ψ φ(x, y) = ψ(x) × φ(y). The tensor product structure on H = L2(Rk+m) is symbolically denoted as H = H1 H2.
Consider now two arbitrary orthonormal bases in spaces Hk , (ej(k)), k = 1, 2.
Then functions (eij = ei(1) ej(2)) form an orthonormal basis in H : any H can be represented as
= cij eij ≡ cij ei(1) ej(2), |
(48) |
where |
|
|cij |2 < ∞. |
(49) |
Consider now two arbitrary Þnite-dimensional Hilbert spaces, H1, H2. For each pair of vectors ψ H1, φ H2, we form a new formal entity denoted by ψ φ. Then we consider the sums = i ψi φi . On the set of such formal sums we can introduce the linear space structure. (To be mathematically rigorous, we have to constraint this set by some algebraic relations to make the operations of addition and multiplication by complex numbers well deÞned.) This construction gives us
the tensor product H = H1 H2. In particular, if we take orthonormal bases in Hk , (ej(k)), k = 1, 2, then (eij = ei(1) ej(2)) form an orthonormal basis in H, anyH can be represented as (48) with (49).
5Here it is convenient to use this symbol, not just write as (x, y) = ψ(x)φ(y).
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The latter representation gives the simplest possibility to deÞne the tensor product of two arbitrary (i.e., may be inÞnite-dimensional) Hilbert spaces as the space of formal series (48) satisfying the condition (49).
Besides the notion of the tensor product of states, we shall also use the notion of the tensor product of operators. Consider two linear operators ai : Hi → Hi , i = 1, 2. Their tensor product a ≡ a1 a2 : H → H is deÞned starting with the tensor products of two vectors:
a ψ φ = (a1ψ) (a2φ).
Then it is extended by linearity. By using the coordinate representation (48) the tensor product of operators can be represented as
a = cij aeij ≡ cij a1ei(1) a2ej(2), |
(50) |
If operators ai , i = 1, 2, are represented by matrices (akl(i)), with respect to the Þxed bases, then the matrix (akl.nm) of the operator a with respect to the tensor product of these bases can be easily calculated.
In the same way one deÞnes the tensor product of Hilbert spaces, H1, . . . , Hn, denoted by the symbol H = H1 · · · Hn. We start with forming the formal entities ψ1 · · · ψn, where ψj Hj , j = 1, . . . , n. Tensor product space is deÞned as the set of all sums j ψ1j · · · ψnj (which has to be constrained
by some algebraic relations, but we omit such details). Take orthonormal bases in Hk , (ej(k)), k = 1, . . . , n. Then any H can be represented as
= cα eα ≡ |
|
(1) |
(n) |
|
|
α=(j1 |
cj1...jn ej1 |
· · · ejn |
, |
(51) |
|
α |
...jn) |
|
|
|
|
|
|
|
|
where α |cα |2 < ∞.
13 Ketand Bra-Vectors: Dirac’s Symbolism
DiracÕs notations [21] are widely used in quantum information theory. Vectors of H are called ket-vectors, they are denoted as |ψ . The elements of the dual space H of H, the space of linear continuous functionals on H, are called bra-vectors, they are denoted as ψ|.
Originally the expression ψ|φ was used by Dirac for the duality form between H and H, i.e., ψ|φ is the result of application of the linear functional ψ| to the vector |φ . In mathematical notation it can be written as follows. Denote the functional ψ| by f and the vector |φ by simply φ. Then ψ|φ ≡ f (φ). To simplify the model, later Dirac took the assumption that H is Hilbert space, i.e., the H can be identiÞed with H. We remark that this assumption is an axiom simplifying