Distinguishing Ordered Phases using Machine Learning and Classical Shadows

• URL: http://arxiv.org/abs/2501.17837v1
• Date: Wed, 29 Jan 2025 18:35:40 GMT
• Title: Distinguishing Ordered Phases using Machine Learning and Classical Shadows
• Authors: Leandro Morais, Tiago Pernambuco, Rodrigo G. Pereira, Askery Canabarro, Diogo O. Soares-Pinto, Rafael Chaves,
• Abstract summary: This work proposes a framework for identifying quantum phase transitions by combining classical shadows with unsupervised machine learning.<n>Even with few qubits, we can effectively distinguish between the different phases of the Hamiltonian models.
• Score: 0.3769303106863453
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classifying phase transitions is a fundamental and complex challenge in condensed matter physics. This work proposes a framework for identifying quantum phase transitions by combining classical shadows with unsupervised machine learning. We use the axial next-nearest neighbor Ising model as our benchmark and extend the analysis to the Kitaev-Heisenberg model on a two-leg ladder. Even with few qubits, we can effectively distinguish between the different phases of the Hamiltonian models. Moreover, given that we only rely on two-point correlator functions, the classical shadows protocol enables the cost of the analysis to scale logarithmically with the number of qubits, making our approach a scalable and efficient way to study phase transitions in many-body systems.

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