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arXiv 1101.4359quant-ph
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量子加速机制

Mechanism of the quantum speed-up

Giuseppe Castagnoli

备注：
Added "light and shade" effects, improving understandability; positioned the approach against state of the art; 19 pages
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摘要：

我们解释的机制的量子加速量子算法需要更少的计算步骤比他们的经典等价物-一个家庭的算法。鲍勃选择一个函数，并把计算它的黑盒交给爱丽丝。爱丽丝在不知道鲍勃选择的情况下，应该找到函数的一个特征（e。G.它的周期）通过计算不同参数的值。Bob的选择和Alice找到的解决方案之间自然存在相关性。我们表明，在量子算法中，这种相关性变得量子化。这突出了一个被忽视的测量问题：在两个测量之间共享相关（因此冗余）测量结果的确定。解决这个问题解释了加速。一切都像爱丽丝一样，通过在算法的最后读取解决方案，促成了鲍勃的初始选择，为它的一半在量子叠加的所有可能的方式采取这一半。这个贡献，回到运行算法之前，鲍勃的选择位于哪里，变成爱丽丝提前知道这个选择的一半。量子算法是所有可能的方法的量子叠加，这些方法取鲍勃选择的一半，并给出它的高级知识，经典地计算缺少的一半。这产生了相对于经典情况的加速，在经典情况下，最初，鲍勃的选择对爱丽丝来说是完全未知的。

英文摘要：

We explain the mechanism of the quantum speed-up - quantum algorithms requiring fewer computation steps than their classical equivalent - for a family of algorithms. Bob chooses a function and gives to Alice the black box that computes it. Alice, without knowing Bob's choice, should find a character of the function (e. g. its period) by computing its value for different arguments. There is naturally correlation between Bob's choice and the solution found by Alice. We show that, in quantum algorithms, this correlation becomes quantum. This highlights an overlooked measurement problem: sharing between two measurements the determination of correlated (thus redundant) measurement outcomes. Solving this problem explains the speed-up. All is like Alice, by reading the solution at the end of the algorithm, contributed to the initial choice of Bob, for half of it in quantum superposition for all the possible ways of taking this half. This contribution, back evolved to before running the algorithm, where Bob's choice is located, becomes Alice knowing in advance half of this choice. The quantum algorithm is the quantum superposition of all the possible ways of taking half of Bob's choice and, given the advanced knowledge of it, classically computing the missing half. This yields a speed-up with respect to the classical case where, initially, Bob's choice is completely unknown to Alice.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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