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020 _a9783031025198
_9978-3-031-02519-8
024 7 _a10.1007/978-3-031-02519-8
_2doi
050 4 _aQA1-939
072 7 _aPB
_2bicssc
072 7 _aMAT000000
_2bisacsh
072 7 _aPB
_2thema
082 0 4 _a510
_223
100 1 _aCruz-Santos, William.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981405
245 1 0 _aApproximability of Optimization Problems through Adiabatic Quantum Computation
_h[electronic resource] /
_cby William Cruz-Santos, Guillermo Morales-Luna.
250 _a1st ed. 2014.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2014.
300 _aXV, 97 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Quantum Computing,
_x1945-9734
505 0 _aPreface -- Acknowledgments -- Introduction -- Approximability of NP-hard Problems -- Adiabatic Quantum Computing -- Efficient Hamiltonian Construction -- AQC for Pseudo-Boolean Optimization -- A General Strategy to Solve NP-Hard Problems -- Conclusions -- Bibliography -- Authors' Biographies.
520 _aThe 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 large enough, then the system remains close to its ground state. An AQC algorithm uses the adiabatic theorem to approximate the ground state of the final Hamiltonian that corresponds to the solution of the given optimization problem. In this book, we investigate the computational simulation of AQC algorithms applied to the MAX-SAT problem. A symbolic analysis of the AQC solution is given in order to understand the involved computational complexity of AQC algorithms. This approach can be extended to other combinatorial optimization problems and can be used for the classical simulation of an AQC algorithm where a Hamiltonian problem is constructed. This construction requires the computation of a sparse matrix of dimension 2n × 2n, by means of tensor products, where n is the dimension of the quantum system. Also, a general scheme to design AQC algorithms is proposed, based on a natural correspondence between optimization Boolean variables and quantum bits. Combinatorial graph problems are in correspondence with pseudo-Boolean maps that are reduced in polynomial time to quadratic maps. Finally, the relation among NP-hard problems is investigated, as well as its logical representability, and is applied to the design of AQC algorithms. It is shown that every monadic second-order logic (MSOL) expression has associated pseudo-Boolean maps that can be obtained by expanding the given expression, and also can be reduced to quadratic forms. Table of Contents: Preface / Acknowledgments / Introduction / Approximability of NP-hard Problems / Adiabatic Quantum Computing / Efficient Hamiltonian Construction / AQC for Pseudo-Boolean Optimization / A General Strategy to Solve NP-Hard Problems / Conclusions / Bibliography / Authors' Biographies.
650 0 _aMathematics.
_911584
650 0 _aQuantum computers.
_93985
650 0 _aQuantum physics.
_981406
650 1 4 _aMathematics.
_911584
650 2 4 _aQuantum Computing.
_910080
650 2 4 _aQuantum Physics.
_981407
700 1 _aMorales-Luna, Guillermo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981408
710 2 _aSpringerLink (Online service)
_981409
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031013911
776 0 8 _iPrinted edition:
_z9783031036477
830 0 _aSynthesis Lectures on Quantum Computing,
_x1945-9734
_981410
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02519-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c85171
_d85171