An introduction to quantum annealing
An introduction to quantum annealing
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量子退火简介
DOI：
10.1051/ita/2011013
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
D. Tamascelli
中科院分区：
文献类型：
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作者：
D. Falco;D. Tamascelli
量子退火，或量子随机优化，是一个经典的随机算法，它提供了很好的算法来解决困难的优化问题。该算法由量子系统的行为提出，是经典和量子计算机科学之间复杂交叉污染的一个例子。在这篇综述论文中，我们说明了如何通过量子计算来解决组合问题，并提出了量子退火提供的算法的一些例子。我们还提出了量子耗散（作为一种替代虚时间演化）的任务，驱动量子系统向其最低能量状态的应用程序的初步结果。
Quantum annealing, or quantum stochastic optimization, is a classical randomized algorithm which provides good heuristics for the solution of hard optimization problems. The algorithm, suggested by the behaviour of quantum systems, is an example of proficuous cross contamination between classical and quantum computer science. In this survey paper we illustrate how hard combinatorial problems are tackled by quantum computation and present some examples of the heuristics provided by quantum annealing. We also present preliminary results about the application of quantum dissipation (as an alternative to imaginary time evolution) to the task of driving a quantum system toward its state of lowest energy.