- •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
suai.ru/our-contacts |
quantum machine learning |
Density Hypercubes, Higher Order Interference and Hyper-decoherence |
147 |
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(14)
Normalised maps of density hypercubes form a sub-SMC of DD(fHilb), which we refer to as the normalised sub-category. Sub-normalised maps of density hyper-
cubes can be defined analogously by requiring the corresponding CP map to be
trace non-increasing: they also form a sub-SMC of DD(fHilb), which we refer to as the sub-normalised sub-category.
Despite the presence of several kinds of discarding maps, the following results shows that the sub-normalised sub-category is causal [3], or equivalently that that the normalised sub-category is terminal [6, 7].
Proposition 1. The process theory DD(fHilb) is causal, in the following sense: for every object DD(H), the only e ect DD(H) → R+ in DD(fHilb) which yields the scalar 1 on all normalised states of DD(H) is the “forest” discarding map of density hypercubes DD(H).
3 Decoherence and Hyper-decoherence
So far, we have constructed a symmetric monoidal category, which is enriched in convex cones and comes equipped with an environment structure provid-
ing a notion of normalization. The final ingredients necessary for the definition of the categorical probabilistic theory of density hypercubes is the demonstra-
tion that classical systems and quantum systems arise in the Karoubi envelope of DD(fHilb) by choosing some suitable family of decoherence and hyperdecoherence maps.
3.1Decoherence to Classical Theory
Consider a finite-dimensional Hilbert space H and a classical structure on it, associated with some orthonormal basis (|ψx )x X . We define the -decoherence
map dec on the density hypercube DD(H) to be the following morphism in DD(fHilb):
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The dec map defined above is idempotent, so it can be used to define classical systems via the Karoubi envelope construction—in the same way as ordinary
suai.ru/our-contacts |
quantum machine learning |
148 S. Gogioso and C. M. Scandolo
decoherence maps gives rise to classical systems in quantum theory. It should be noted that decoherence maps defined this way are sub-normalised but not normalised, so that the hyperquantum-to-classical transition in the theory of density hypercubes is not deterministic; we defer further discussion of this point to the next sub-section on hyper-decoherence.
Proposition 2. Let Split(DD(fHilb)) be the Karoubi envelope of DD(fHilb), and write Split(DD(fHilb))K for the full subcategory of Split(DD(fHilb)) spanned by objects in the form (DD(H), dec). There is an R+-linear monoidal equivalence of categories between Split(DD(fHilb))K and the probabilistic theory R+-Mat of classical systems. Furthermore, classical stochastic maps correspond
to the maps in Split(DD(fHilb))K normalised with respect to the discarding maps |
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(16) |
H
3.2Hyper-decoherence to Quantum Theory
If is a classical structure on a density hypercube DD(H), we define the -
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to be the following map of density hypercubes: |
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Hyper-decoherence maps are idempotent, and hence we can consider the full subcategory C of the Karoubi envelope Split(DD(fHilb)) spanned by objects in the form (DD(H), hypdec): doing so allows us to prove that the hyperdecoherence maps defined above truly provide the desired “hyper-quantum–to– quantum” decoherence, as considered by [18, 20].
Proposition 3. Let Split(DD(fHilb)) be the Karoubi envelope of DD(fHilb), and write Split(DD(fHilb))Q for the full subcategory of Split(DD(fHilb)) spanned
suai.ru/our-contacts |
quantum machine learning |
Density Hypercubes, Higher Order Interference and Hyper-decoherence |
149 |
by objects in the form (DD(H), hypdec). There is an R+-linear monoidal equivalence of categories between Split(DD(fHilb))Q and the probabilistic theory CPM(fHilb) of quantum systems and CP maps between them. Furthermore, trace-preserving CP maps correspond to the maps in Split(DD(fHilb))Q nor-
malised with respect to the discarding maps |
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Taking the double-dilation construction together with the content of Propositions 2 and 3, we come to the following definition of a categorical probabilistic theory [11] of density hypercubes.
Definition 1. The categorical probabilistic theory of density hypercubes DH(fHilb) is defined the be the full sub-SMC of Split(DD(fHilb)) spanned by objects in the following form:
–the density hypercubes (DD(H), idDD(H));
–the quantum systems (DD(H), hypdec), for all classical structures on H;
–the classical systems (DD(H), dec), for all classical structures on H.
The environment structure for the categorical probabilistic theory is given by the
discarding maps DD(H), (DD(H),hypdec ) and (DD(H),dec ) respectively. The classical sub-category for the categorical probabilistic theory is the full sub-
SMC spanned by the classical systems.
The hyper-quantum–to–classical and hyper-quantum–to–quantum decoherence maps of density hypercubes play well together with the quantum–to– classical decoherence map of quantum theory: the decoherence map dec : (DD(H), idDD(H)) → (DD(H), dec) of density hypercubes factors, as one would expect, into the hyper-decoherence map hypdec, from (DD(H), idDD(H)) to (DD(H), hypdec), followed by the decoherence map of quantum systems dec, from (DD(H), hypdec) to (DD(H), dec). From this, it is clear that the reason why hyper-quantum–to–classical transition was sub-normalised is that the hyper-quantum–to–quantum transition itself is sub-normalised (cf. Appendix B).
The sub-normalisation of hyper-decoherence maps is a sign that the theory of density hypercubes presented here is still partially incomplete, and that some suitable extension will need to be researched in the future. What we know for sure is that the current theory does not satisfy the no-restriction condition on e ects, and that an extension in which hyper-decoherence maps are normalised is possible: the additional e ect needed by normalisation exists in CPM(fHilb) and is non-negative on all states of DD(fHilb) (cf. Appendix B). In line with the recent no-go theorem of [20], preliminary considerations seem to indicated that the addition of said e ect would mean that the theory no longer satisfies purification.