Zipping many-body quantum states: a scalable approach to diagonal entropy
Yu-Hsueh Chen, Tarun Grover

TL;DR
This paper introduces a scalable method using Lempel-Ziv compression to estimate diagonal entropy in many-body quantum states, enabling efficient analysis of phase transitions and symmetry breaking with polynomial resources.
Contribution
It demonstrates that Lempel-Ziv compression can accurately estimate diagonal entropy density in quantum many-body systems, offering a scalable alternative to exponential tomographic methods.
Findings
Compression accurately recovers entropy density in various models.
Diagonal entropy density exhibits specific scaling near critical points.
Method requires only polynomially many images for reliable estimates.
Abstract
The outcomes of projective measurements on a quantum many-body system in a chosen basis are inherently probabilistic. The Shannon entropy of this probability distribution (the "diagonal entropy") often reveals universal features, such as the existence of a quantum phase transition. A brute-force tomographic approach to estimating this entropy scales exponentially with the system size. Here, we explore using the Lempel-Ziv lossless image compression algorithm as an efficient, scalable alternative, readily implementable in a quantum gas microscope or programmable quantum devices. We test this approach on several examples: one-dimensional quantum Ising model, and two-dimensional states that display conventional symmetry breaking due to quantum fluctuations, or strong-to-weak symmetry-breaking due to local decoherence. We also employ the diagonal mixed state to put constraints on the phase…
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Taxonomy
TopicsQuantum many-body systems · Quantum Computing Algorithms and Architecture · Advanced Thermodynamics and Statistical Mechanics
