Quantum-inspired memory-enhanced stochastic algorithms
John Realpe-Gomez and Nathan Killoran
Received Date: 13th June 19
Stochastic models are highly relevant tools in science, engineering, and society. Recent worksuggests emerging quantum computing technologies can substantially decrease the memory require-ments 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, weshow that it is possible to develop quantum-inspired classical algorithms that require much lessmemory than the best classical algorithms known to date. Being classical, such algorithms could beimplemented in state-of-the-art high-performance computers, which could potentially enhance thestudy of large-scale complex systems. Furthermore, since memory is the main bottleneck limitingthe performance of classical supercomputers in one of the most promising avenues to demonstratequantum ‘supremacy’, we expect adaptations of these ideas may potentially further raise the barfor near-term quantum computers to reach such a milestone
Read in full at arXiv.
This is an abstract of a preprint hosted on an independent third party site. It has not been peer reviewed but is currently under consideration at Nature Communications.