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PRINCIPLES OF QUANTUM ARTIFICIAL INTELLIGENCE.
In this book, we introduce quantum computation and its application to AI. We highlight problem solving and knowledge representation framework. Based on information theory, we cover two main principles of quantum computation - Quantum Fourier transform and Grover search. Then, we indicate how these t...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
WSPC,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Preface; Contents; 1. Introduction; 1.1 Artificial Intelligence; 1.2 Motivation and Goals; 1.3 Guide to the Reader; 1.4 Content; 1.4.1 Classical computation; 1.4.2 Quantum computation; 2. Computation; 2.1 Entscheidungsproblem; 2.1.1 Cantor's diagonal argument; 2.1.2 Reductio ad absurdum; 2.2 Complexity Theory; 2.2.1 Decision problems; 2.2.2 P and NP; 2.3 Church-Turing Thesis; 2.3.1 Church-Turing-Deutsch principle; 2.4 Computers; 2.4.1 Analog computers; 2.4.2 Digital computers; 2.4.3 Von Neumann architecture; 3. Problem Solving; 3.1 Knowledge Representation; 3.1.1 Rules.
- 3.1.2 Logic-based operators3.1.3 Frames; 3.1.4 Categorial representation; 3.1.5 Binary vector representation; 3.2 Production System; 3.2.1 Deduction systems; 3.2.2 Reaction systems; 3.2.3 Conflict resolution; 3.2.4 Human problem-solving; 3.2.5 Example; 3.3 Sub-Symbolic Models of Problem-Solving; 3.3.1 Proto logic; 3.3.2 Binding problem; 3.3.3 Icons; 3.3.4 Euclidian geometry of the world; 4. Information; 4.1 Information and Thermodynamics; 4.1.1 Dice model; 4.1.2 Entropy; 4.1.3 Maxwell paradox and information; 4.1.4 Information theory; 4.2 Hierarchical Structures; 4.2.1 Example of a taxonomy.
- 4.3 Information and Measurement4.3.1 Information measure I; 4.3.2 Nature of information measure; 4.3.3 Measurement of angle; 4.3.4 Information and contour; 4.4 Information and Memory; 4.5 Sparse code for Sub-symbols; 4.5.1 Sparsification based on unary sub-vectors; 4.6 Deduction Systems and Associative Memory; 4.6.1 Taxonomic knowledge organization; 5. Reversible Algorithms; 5.1 Reversible Computation; 5.2 Reversible Circuits; 5.2.1 Boolean gates; 5.2.2 Reversible Boolean gates; 5.2.3 Toffoli gate; 5.2.4 Circuit; 6. Probability; 6.1 Kolmogorovs Probabilities; 6.1.1 Conditional probability.
- 6.1.2 Bayes's rule6.1.3 Joint distribution; 6.1.3.1 Example; 6.1.4 Naıve Bayes and counting; 6.1.5 Counting and categorization; 6.1.6 Bayesian networks; 6.1.6.1 Example; 6.2 Mixed Distribution; 6.3 Markov Chains; 7. Introduction to Quantum Physics; 7.1 Unitary Evolution; 7.1.1 Schrodinger's cat paradox; 7.1.2 Interpretations of quantum mechanics; 7.2 Quantum Mechanics; 7.2.1 Stochastic Markov evolution and unitary evolution; 7.3 Hilbert Space; 7.3.1 Spectral representation*; 7.4 Quantum Time Evolution; 7.5 Compound Systems; 7.6 Von Neumann Entropy; 7.7 Measurement; 7.7.1 Observables.
- 7.7.2 Measuring a compound system7.7.3 Heisenberg's uncertainty principle*; 7.8 Randomness; 7.8.1 Deterministic chaos; 7.8.2 Kolmogorov complexity; 7.8.3 Humans and random numbers; 7.8.4 Randomness in quantum physics; 8. Computation with Qubits; 8.1 Computation with one Qubit; 8.2 Computation with m Qubit; 8.3 Matrix Representation of Serial and Parallel Operations; 8.4 Entanglement; 8.5 Quantum Boolean Circuits; 8.6 Deutsch Algorithm; 8.7 Deutsch Jozsa Algorithm; 8.8 Amplitude Distribution; 8.8.1 Cloning; 8.8.2 Teleportation; 8.9 Geometric Operations; 9. Periodicity; 9.1 Fourier Transform.