[
    {
        "id": "authors:ynhzn-84626",
        "collection": "authors",
        "collection_id": "ynhzn-84626",
        "cite_using_url": "https://authors.library.caltech.edu/records/ynhzn-84626",
        "type": "publication_erratum",
        "title": "Author Correction: Improved machine learning algorithm for predicting ground state properties",
        "author": [
            {
                "family_name": "Lewis",
                "given_name": "Laura",
                "clpid": "Lewis-Laura"
            },
            {
                "family_name": "Huang",
                "given_name": "Hsin-Yuan",
                "orcid": "0000-0001-5317-2613",
                "clpid": "Huang-Hsin-Yuan"
            },
            {
                "family_name": "Tran",
                "given_name": "Viet T.",
                "clpid": "Tran-Viet-T"
            },
            {
                "family_name": "Lehner",
                "given_name": "Sebastian",
                "orcid": "0000-0002-7562-8172",
                "clpid": "Lehner-Sebastian"
            },
            {
                "family_name": "Kueng",
                "given_name": "Richard",
                "orcid": "0000-0002-8291-648X",
                "clpid": "Kueng-Richard"
            },
            {
                "family_name": "Preskill",
                "given_name": "John",
                "orcid": "0000-0002-2421-4762",
                "clpid": "Preskill-J"
            }
        ],
        "abstract": "<p>Finding the ground state of a quantum many-body system is a fundamental problem in quantum physics. In this work, we give a classical machine learning (ML) algorithm for predicting ground state properties with an inductive bias encoding geometric locality. The proposed ML model can efficiently predict ground state properties of an&nbsp;<em>n</em>-qubit gapped local Hamiltonian after learning from only <span>\ud835\udcaa</span><span class=\"mathjax-tex\"><span class=\"MathJax_SVG\"><span class=\"MJX_Assistive_MathML\">(log\u2061(<span><em>n</em></span>))</span></span></span><span> data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require \ud835\udcaa</span><span class=\"mathjax-tex\"><span class=\"MathJax_SVG\"><span class=\"MJX_Assistive_MathML\">(<span><em>n\u1d9c</em></span>)</span></span></span><span>&nbsp;data for a large constant&nbsp;</span><em>c</em><span>. Furthermore, the training and prediction time of the proposed ML model scale as \ud835\udcaa</span><span class=\"mathjax-tex\"><span class=\"MathJax_SVG\"><span class=\"MJX_Assistive_MathML\">(<span><em>n </em></span>log <span><em>n</em></span>)</span></span></span><span>&nbsp;in the number of qubits&nbsp;</span><em>n</em><span>. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.</span></p>",
        "doi": "10.1038/s41467-024-46164-4",
        "issn": "2041-1723",
        "publisher": "Nature Publishing Group",
        "publication": "Nature Communications",
        "publication_date": "2024-02-26",
        "volume": "15",
        "pages": "1740"
    }
]