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arXiv:2110.10214 · Physical review. B./Physical review. B

Persistent homology of quantum entanglement

Bart Olsthoorn

Published Mar 31, 2023• 13 citations
arXivHTMLPDF

Abstract

What shapes of entanglement are present in quantum states? Persistent homology provides an answer that contains both topological and geometrical information. This is a relatively new method from the field of topological data analysis, where shapes in discrete data are captured at varying length scales. In this case, the length scale is based on quantum mutual information, and the method is used to detect quantum phase transitions.

DOI
10.1103/physrevb.107.115174

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What shapes of entanglement are present in quantum states? Persistent homology provides an answer that contains both topological and geometrical information. This is a relatively new method from the field of topological data analysis, where shapes in discrete data are captured at varying length scales. In this case, the length scale is based on quantum mutual information, and the method is used to detect quantum phase transitions.
Quantum state tomography (QST) is a crucial technique for characterizing the state of a quantum system. However, the data generated by QST—typically a large, dense measurement matrix—presents significant challenges for analysis and interpretation. This work proposes a novel approach leveraging Topological Data Analysis (TDA), specifically persistent homology, to extract meaningful insights from these complex quantum state data. We demonstrate that the underlying topological structure of the measurement matrices can reveal fundamental characteristics of the quantum state, including entanglement properties and the degree of coherence. The core idea involves treating the measurement matrix as a high-dimensional point cloud and applying persistent homology to uncover persistent loops and connected components, which directly correspond to topological features of the quantum state. This method offers a new perspective on QST data analysis, potentially leading to improved state characterization and a deeper understanding of quantum entanglement. The application of TDA provides a robust and geometric interpretation of the measurement data, moving beyond traditional statistical methods.
Extracting useful information from large data sets can be a daunting task. Topological methods for analyzing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying such topological features -- connected components, holes, or voids -- and for determining how such features persist as the data is viewed at different scales. This paper provides quantum algorithms for calculating Betti numbers in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speedup over classical algorithms for topological data analysis.
Extracting useful information from large data sets can be a daunting task. Topological methods for analysing data sets provide a powerful technique for extracting such information. Persistent homology is a sophisticated tool for identifying topological features and for determining how such features persist as the data is viewed at different scales. Here we present quantum machine learning algorithms for calculating Betti numbers--the numbers of connected components, holes and voids--in persistent homology, and for finding eigenvectors and eigenvalues of the combinatorial Laplacian. The algorithms provide an exponential speed-up over the best currently known classical algorithms for topological data analysis.
We introduce "quantum barcodes," a theoretical framework that applies persistent homology to classify topological phases in quantum many-body systems. By mapping quantum states to classical data points through strategic observable measurements, we create a "quantum state cloud" analyzable via persistent homology techniques. Our framework establishes that quantum systems in the same topological phase exhibit consistent barcode representations with shared persistent homology groups over characteristic intervals. We prove that quantum phase transitions manifest as significant changes in these persistent homology features, detectable through discontinuities in the persistent Dirac operator spectrum. Using the SSH model as a demonstrative example, we show how our approach successfully identifies the topological phase transition and distinguishes between trivial and topological phases. While primarily developed for symmetry-protected topological phases, our framework provides a mathematical connection between persistent homology and quantum topology, offering new methods for phase classification that complement traditional invariant-based approaches.