I survey, for a general scientific audience, three decades of research into which sorts of problems admit exponential speedups via quantum computers -- from the classics (like the algorithms of Simon and Shor), to the breakthrough of Yamakawa and Zhandry from April 2022. I discuss both the quantum circuit model, which is what we ultimately care about in practice but where our knowledge is radically incomplete, and the so-called oracle or black-box or query complexity model, where we've managed to achieve a much more thorough understanding that then informs our conjectures about the circuit model. I discuss the strengths and weaknesses of switching attention to sampling tasks, as was done in the recent quantum supremacy experiments. I make some skeptical remarks about widely-repeated claims of exponential quantum speedups for practical machine learning and optimization problems. Through many examples, I try to convey the "law of conservation of weirdness," according to which every problem admitting an exponential quantum speedup must have some unusual property to allow the amplitude to be concentrated on the unknown right answer(s).
翻译:我调查了30年的研究, 研究的种类 通过量子计算机, 从经典(如西蒙和Shor的算法), 到山川和赞德里2022年4月的突破。 我讨论了量子电路模型, 我们最终关心的是量子电路模型, 但实际上我们的知识是完全不完整的, 以及所谓的神器或黑盒或查询复杂模型, 我们设法取得了更彻底的理解, 然后将电路模型的猜想告知我们的猜测。 我讨论了将注意力转向抽样任务的长处和短处, 就像最近的量子至上实验一样。 我有些怀疑性的评论, 广泛重复了指数量子加速的要求, 以实际的机器学习和优化问题。 我通过许多例子, 试图传达“ 奇异的保存法 ”, 根据这个例子, 每一个接受指数量子加速的问题都必须有一些不寻常的特性, 以便让振动的注意力集中在未知的右答案上。