TL;DR
This paper introduces a new compact neural-network quantum state (NQS) representation for Jastrow and stabilizer states, requiring fewer hidden units and maintaining high expressiveness, which could enhance the understanding and simulation of many-body quantum systems.
Contribution
The authors develop an exact, compact NQS representation for Jastrow and stabilizer states with at most N-1 hidden units, unifying these classes and improving over previous sparse constructions.
Findings
Requires at most N-1 hidden units for exact representation
Unifies Jastrow and stabilizer states in a single NQS framework
Provides insights into the expressiveness of compact NQS models
Abstract
Neural-network quantum states (NQS) have become a powerful tool in many-body physics. Of the numerous possible architectures in which neural-networks can encode amplitudes of quantum states the simplicity of the Restricted Boltzmann Machine (RBM) has proven especially useful for both numerical and analytical studies. In particular devising exact NQS representations for important classes of states, like Jastrow and stabilizer states, has provided useful clues into the strengths and limitations of the RBM based NQS. However, current constructions for a system of spins generate NQS with hidden units that are very sparsely connected. This makes them rather atypical NQS compared to those commonly generated by numerical optimisation. Here we focus on compact NQS, denoting NQS with a hidden unit density but with system-extensive hidden-visible unit…
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