QubitLogic
arXiv

Fast Algorithms for Stoquastic Spin Systems

We establish a general framework for developing fast sampling and counting algorithms for stoquastic spin systems at high temperature. Our framework is based on a rapidly mixing Markov chain for polymer models and a subcritical percolation process for sampling individual...

We apply our framework to obtain fast algorithms for approximating the partition function and sampling from the thermal distribution of general stoquastic spin systems.

Why this matters

Understanding and efficiently simulating stoquastic spin systems is crucial in studying quantum materials and developing quantum algorithms. Improved algorithms can lead to better approximations and insights into these complex systems, which are relevant for various scientific fields.

What they actually achieved

The authors established a framework for creating fast sampling and counting algorithms for stoquastic spin systems at high temperatures. This framework was applied to several models, including general stoquastic spin systems and Heisenberg models on bipartite graphs, to improve inverse temperature bounds.

What they did not achieve

The research does not claim any low-temperature results, which are often of significant interest in quantum physics. Additionally, there is no indication that these algorithms have been implemented in practical scenarios or tested outside theoretical models.

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