Generating and analyzing small-size datasets to explore physical observables in quantum Ising systems

TL;DR

This paper analyzes datasets from simulations of 2D quantum Ising models at zero temperature, exploring how physical observables like energy and entanglement entropy evolve with system size and external magnetic fields.

Contribution

It introduces a comprehensive dataset and analysis framework for quantum spin systems, highlighting size-dependent fluctuations and correlations relevant to quantum phase transitions.

Findings

Fluctuations increase with system size, indicating quantum phase transition onset.

Local interactions dominate spin correlations, but larger systems show complex patterns.

Dataset enables future machine learning studies for phase transition prediction.

Abstract

We propose a detailed analysis of datasets generated from simulations of two-dimensional quantum spin systems using the quantum Ising model at absolute zero temperature. Our focus is on examining how fundamental physical properties, energy, magnetization, and entanglement entropy, evolve under varying external transverse magnetic fields and system sizes. From the Quantum Toolbox in Python (QuTiP), we simulate systems with 4, 8, and 16 spins arranged in square lattices, generating extensive datasets with 5000 samples per magnetic field value. The Hamiltonian operator incorporates quantum mechanical effects such as superposition and tunneling, challenging classical interpretations of spin states. We compute extended Pauli operators and construct the Hamiltonian to include spin-spin interactions and transverse field terms. Our analysis reveals that as the system size increases, fluctuations in energy and entanglement entropy become more evident, indicating lifted sensitivity to external perturbations and suggesting the onset of quantum phase transitions. Spin-spin correlation functions demonstrate that interactions are predominantly local, but larger systems exhibit more complex and fluctuating correlations. These findings provide valuable insights into the behavior of quantum spin systems and lay the groundwork for future machine learning applications aimed at predicting physical quantities and identifying phase transitions from a quantum perspective.