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Speedup in Quantum Adiabatic Evolution Algorithm  [PDF]
Joonwoo Bae,Younghun Kwon
Physics , 2002, DOI: 10.1515/zna-2003-0404
Abstract: Quantum adiabatic evolution algorithm suggested by Farhi et al. was effective in solving instances of NP-complete problems. The algorithm is governed by the adiabatic theorem. Therefore, in order to reduce the running time, it is essential to examine the minimum energy gap between the ground level and the next one through the evolution. In this letter, we show a way of speedup in quantum adiabatic evolution algorithm, using the extended Hamiltonian. We present the exact relation between the energy gap and the elements of the extended Hamiltonian, which provides the new point of view to reduce the running time.
A new adiabatic quantum search algorithm  [PDF]
H. Zaraket,V. Bagnulo,J. Kettner,R. Kobes,G. Kunstatter
Physics , 2003,
Abstract: We present a new adiabatic quantum algorithm for searching over structured databases. The new algorithm is optimized using a simplified complexity analysis.
How to Make the Quantum Adiabatic Algorithm Fail  [PDF]
Edward Farhi,Jeffrey Goldstone,Sam Gutmann,Daniel Nagaj
Physics , 2005,
Abstract: The quantum adiabatic algorithm is a Hamiltonian based quantum algorithm designed to find the minimum of a classical cost function whose domain has size N. We show that poor choices for the Hamiltonian can guarantee that the algorithm will not find the minimum if the run time grows more slowly than square root of N. These poor choices are nonlocal and wash out any structure in the cost function to be minimized and the best that can be hoped for is Grover speedup. These failures tell us what not to do when designing quantum adiabatic algorithms.
Experimental implementation of an adiabatic quantum optimization algorithm  [PDF]
Matthias Steffen,Wim van Dam,Tad Hogg,Greg Breyta,Isaac Chuang
Physics , 2003, DOI: 10.1103/PhysRevLett.90.067903
Abstract: We report the realization of a nuclear magnetic resonance computer with three quantum bits that simulates an adiabatic quantum optimization algorithm. Adiabatic quantum algorithms offer new insight into how quantum resources can be used to solve hard problems. This experiment uses a particularly well suited three quantum bit molecule and was made possible by introducing a technique that encodes general instances of the given optimization problem into an easily applicable Hamiltonian. Our results indicate an optimal run time of the adiabatic algorithm that agrees well with the prediction of a simple decoherence model.
Adiabatic Quantum Computation and Deutsch's Algorithm  [PDF]
Saurya Das,Randy Kobes,Gabor Kunstatter
Physics , 2001, DOI: 10.1103/PhysRevA.65.062310
Abstract: We show that by a suitable choice of a time dependent Hamiltonian, Deutsch's algorithm can be implemented by an adiabatic quantum computer. We extend our analysis to the Deutsch-Jozsa problem and estimate the required running time for both global and local adiabatic evolutions.
Adiabatic quantum search algorithm for structured problems  [PDF]
Jérémie Roland,Nicolas J. Cerf
Physics , 2003, DOI: 10.1103/PhysRevA.68.062312
Abstract: The study of quantum computation has been motivated by the hope of finding efficient quantum algorithms for solving classically hard problems. In this context, quantum algorithms by local adiabatic evolution have been shown to solve an unstructured search problem with a quadratic speed-up over a classical search, just as Grover's algorithm. In this paper, we study how the structure of the search problem may be exploited to further improve the efficiency of these quantum adiabatic algorithms. We show that by nesting a partial search over a reduced set of variables into a global search, it is possible to devise quantum adiabatic algorithms with a complexity that, although still exponential, grows with a reduced order in the problem size.
Dynamics of quantum adiabatic evolution algorithm for Number Partitioning  [PDF]
Vadim N. Smelyanskiy,Udo v. Toussaint,Dogan A. Timucin
Physics , 2002,
Abstract: We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size $n$. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxilary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum excitation gap, $g_{\rm min}={\cal O}(n 2^{-n/2})$, corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simulteneous fipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimenssional quantum diffusion in the energy space. This effect provides a general limitation on the power of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.
First order phase transition in the Quantum Adiabatic Algorithm  [PDF]
A. P. Young,S. Knysh,V. N. Smelyanskiy
Physics , 2009, DOI: 10.1103/PhysRevLett.104.020502
Abstract: We simulate the quantum adiabatic algorithm (QAA) for the exact cover problem for sizes up to N=256 using quantum Monte Carlo simulations incorporating parallel tempering. At large N we find that some instances have a discontinuous (first order) quantum phase transition during the evolution of the QAA. This fraction increases with increasing N and may tend to 1 for N -> infinity.
A quantum search algorithm based on partial adiabatic evolution

Zhang Ying-Yu,Hu He-Ping,Lu Song-Feng,

中国物理 B , 2011,
Abstract: This paper presents and implements a specified partial adiabatic search algorithm on a quantum circuit. It studies the minimum energy gap between the first excited state and the ground state of the system Hamiltonian and it finds that, in the case of M=1, the algorithm has the same performance as the local adiabatic algorithm. However, the algorithm evolves globally only within a small interval, which implies that it keeps the advantages of global adiabatic algorithms without losing the speedup of the local adiabatic search algorithm.
A universal adiabatic quantum query algorithm  [PDF]
Mathieu Brandeho,Jérémie Roland
Computer Science , 2014,
Abstract: Quantum query complexity is known to be characterized by the so-called quantum adversary bound. While this result has been proved in the standard discrete-time model of quantum computation, it also holds for continuous-time (or Hamiltonian-based) quantum computation, due to a known equivalence between these two query complexity models. In this work, we revisit this result by providing a direct proof in the continuous-time model. One originality of our proof is that it draws new connections between the adversary bound, a modern technique of theoretical computer science, and early theorems of quantum mechanics. Indeed, the proof of the lower bound is based on Ehrenfest's theorem, while the upper bound relies on the adiabatic theorem, as it goes by constructing a universal adiabatic quantum query algorithm. Another originality is that we use for the first time in the context of quantum computation a version of the adiabatic theorem that does not require a spectral gap.
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