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arXiv 1906.00263quant-ph
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量子启发的记忆增强随机算法

Quantum-inspired memory-enhanced stochastic algorithms

John Realpe-Gómez, Nathan Killoran

备注：
Main text: 13 pages & 4 figures + SI: 8 pages & 3 figures. 37 references. Basic example in Sec. III B 2 implemented in https://github.com/quantumself/quantum-inspired-sampling.git --- this was recently implemented with quantum technologies by Ghafari et al. (see arXiv:1812.04251)
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摘要：

随机模型是科学、工程和社会中高度相关的工具。最近的研究表明，新兴的量子计算技术可以大大降低模拟随机模型的内存需求。在这里，我们表明，这些最近的量子存储增强算法可以实现或近似经典。换句话说，我们表明，有可能开发量子启发的经典算法，需要更少的内存比迄今为止已知的最好的经典算法。这些算法是经典的，可以在最先进的高性能计算机上实现，这可能会增强对大规模复杂系统的研究。此外，由于内存是限制经典超级计算机性能的主要瓶颈，这是证明量子“至高无上”的最有希望的途径之一，我们预计这些想法的适应可能会进一步提高近期量子计算机达到这样一个里程碑的门槛。

英文摘要：

Stochastic models are highly relevant tools in science, engineering, and society. Recent work suggests emerging quantum computing technologies can substantially decrease the memory requirements for simulating stochastic models. Here we show that some of these recent quantum memory-enhanced algorithms can be either implemented or approximated classically. In other words, we show that it is possible to develop quantum-inspired classical algorithms that require much less memory than the best classical algorithms known to date. Being classical, such algorithms could be implemented in state-of-the-art high-performance computers, which could potentially enhance the study of large-scale complex systems. Furthermore, since memory is the main bottleneck limiting the performance of classical supercomputers in one of the most promising avenues to demonstrate quantum 'supremacy', we expect adaptations of these ideas may potentially further raise the bar for near-term quantum computers to reach such a milestone.

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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