  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.
