Approximability of Optimization Problems through Adiabatic Quantum Computation.Material type: TextSeries: eBooks on DemandSynthesis Lectures on Quantum Computing: Publisher: San Rafael : Morgan & Claypool Publishers, 2014Description: 1 online resource (115 p.)ISBN: 9781627055574Subject(s): Adiabatic invariants | Invariants adiabatiques | Quantum computers -- MathematicsGenre/Form: Electronic books.Additional physical formats: Print version:: Approximability of Optimization Problems through Adiabatic Quantum ComputationDDC classification: 004.1 LOC classification: QA76.889Online resources: Click here to view this ebook.
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Preface; Acknowledgments; Introduction; Approximability of NP-hard Problems; Basic Definitions; Probabilistic Proof Systems; Optimization Problems; Approximation Algorithms; Randomized Complexity Classes; The Complexity Class BPP; The Complexity Class RP; The Complexity Class ZPP; Quantum Complexity; Randomness and Determinism; Derandomization of the Class BPP; Derandomization Techniques; Adiabatic Quantum Computing; Basic Definitions; Linear Operators; Quantum States and Evolution; The Adiabatic Theorem; Adiabatic Evolution; Quantum Computation by Adiabatic Evolution; Adiabatic Paths
Geometric Berry PhasesGeometric Quantum Computation; Efficient Hamiltonian Construction; AQC Applied to the MAX-SAT Problem; Satisfiability Problem; AQC Formulation of SAT; Procedural Hamiltonian Construction; Hyperplanes in the Hypercube; The Hamiltonian Operator H_E; The Hamiltonian Operator H_Z_; AQC for Pseudo-Boolean Optimization; Basic Transformations ; AQC for Quadratic Pseudo-Boolean Maps; Hadamard Transform; _x Transform; k-Local Hamiltonian Problems; Reduction of Graph Problems to the 2-Local Hamiltonian Problem; Graph Structures and Optimization Problems; Relational Signatures
First-Order LogicSecond-Order Logic; Monadic Second-Order Logic Decision and Optimization Problems; MSOL Optimization Problems and Pseudo-Boolean Maps; A General Strategy to Solve NP-Hard Problems; Background; Basic Notions; Tree Decompositions; Procedural Modification of Tree Decompositions; Modification by the Addition of an Edge; Iterative Modification; Branch Decompositions; Comparison of Time Complexities; A Strategy to Solve NP-Hard Problems; Dynamic Programming Approach; The Courcelle Theorem; Examples of Second-Order Formulae; Dynamic Programming Applied to NP-Hard Problems
The Classical Ising ModelQuantum Ising Model; Conclusions; Bibliography; Authors'' Biographies
The adiabatic quantum computation (AQC) is based on the adiabatic theorem to approximate solutions of the Schrödinger equation. The design of an AQC algorithm involves the construction of a Hamiltonian that describes the behavior of the quantum system. This Hamiltonian is expressed as a linear interpolation of an initial Hamiltonian whose ground state is easy to compute, and a final Hamiltonian whose ground state corresponds to the solution of a given combinatorial optimization problem. The adiabatic theorem asserts that if the time evolution of a quantum system described by a Hamiltonian is l
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