# Quantum-inspired memory-enhanced stochastic algorithms

## TL;DR

This paper introduces quantum-inspired classical algorithms that significantly reduce memory requirements for simulating stochastic models, potentially enhancing classical supercomputing and impacting quantum supremacy benchmarks.

## Contribution

It demonstrates that quantum memory advantages can be approximated classically, enabling more efficient simulations on existing high-performance computers.

## Key findings

- Quantum-inspired algorithms require less memory than traditional methods.
- Classical implementations can approximate quantum algorithms for stochastic models.
- Potential to improve classical simulation efficiency and influence quantum supremacy efforts.

## Abstract

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.