量子计算入门

本文用于记录我学习量子计算的过程。

Resources

Web Pages

Books

  • Introduction to the Theory of Computation

    Introduction_to_the_Theory_of_Computation.jpg 这本书用来补一下有关计算复杂度的知识。 很意外,这本书的Part 1 部分,填补了之前读EOPL的一些关于Parser的空白。关于自动机,正则表达式,CFG的讲解一气呵成。Part2部分对图灵机有了更多的了解,halting啥的不再停留在表面的认识。读完Part3之后对复杂性有了新的的认识,之前看到有一个书评说,如果你在别人高谈阔论P,NP,NP-complete等问题时感到一脸懵逼,请立即抱起这本书,这里有你想要的答案。

  • Quantum Computing Since Democritus

    Quantum_Computing_Since_Democritus.jpg

    Quantum_Computing_Since_Democritus.jpg

    这本书没法评价。

    因为压根没读懂,水平有限,摊手......

    前几章跟Quantum Computing的关系不大,开篇的冷笑话,真的好冷......以至于我真的只记住了那句话(有兴趣的话,可以去看看作者的博客):

    But if quantum mechanics isn't physics in the usual sense - if it's not about matter, or energy, or waves, or particles -then what is it about? From my perspective, it's about information and probabilities and observables, and how they relate to each other.

    我能体会到作者独特的视角,无奈,自己相关的基础并不扎实,强行读到了第十章,后面的部分只是草草翻了下。后来偶然在网上看到了别人写的一篇review,深有同感。总的来说,如果你看到chapter1~8的标题之后,确认你对相关内容不那么陌生(不是熟悉或精通),那么可以断定这是一本非常值得一读的书,否则真的很难跟上作者的脚步(当然,你也可以像我一样,不妨先读读试试~)。Anyway,即使只读了前十章,也依然收获颇丰,许多亮点与前面提到的那篇review有许多共通之处。这里只说我感受最深的一点:

    There are two ways to teach quantum mechanics. The first way - which for most physicists today is still the only way - follows the historical order in which the ideas were discovered... The second way to teach quantum mechanics eschews a blow-by-blow account of its discovery, and instead starts directly from the conceptual core - namely, a certain generalization of the laws of probability to allow minus signs )and more generally, complex numbers).

    作者首先提到了目前学习量子原理的两种方式(作者在书中采取的是后者),然后说道:

    Quantum mechanics is what you would inevitably come up with if you started from probability theory, and then said, let's try to generalize it so that the numbers we used to call "probabilities" can be negative numbers. As such, the theory could have been invented by mathematicians in the nineteenth century without any input from experiment. It wasn't, but it could have been.

    是的,实验先于理论。另外两个类似的例子是进化论狭义相对论。读到这里时,我联想到的是目前深度学习的现状,何其相似。作者认为:

    More often than not, the only reason we need experiments is that we're not smart enough.