Improved machine learning algorithm for predicting ground state properties

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 predi...

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Published in:	Nature communications Vol. 15; no. 1; pp. 895 - 8
Main Authors:	Lewis, Laura, Huang, Hsin-Yuan, Tran, Viet T., Lehner, Sebastian, Kueng, Richard, Preskill, John
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 30.01.2024 Nature Publishing Group Nature Portfolio
Subjects:	639/705/117 639/766/483/1139 639/766/483/481 Algorithms CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS Computer science Ground state Humanities and Social Sciences Learning algorithms Machine learning MATHEMATICS AND COMPUTING multidisciplinary Quantum information Quantum mechanics Quantum theory Qubits (quantum computing) Science Science & Technology Science (multidisciplinary)
ISSN:	2041-1723, 2041-1723
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Abstract	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 n -qubit gapped local Hamiltonian after learning from only O ( log ( n ) ) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require O ( n c ) data for a large constant c . Furthermore, the training and prediction time of the proposed ML model scale as O ( n log n ) in the number of qubits n . Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. Recent work proposed a machine learning algorithm for predicting ground state properties of quantum many-body systems that outperforms any non-learning classical algorithm but requires extensive training data. Lewis et al. present an improved algorithm with exponentially reduced training data requirements.
AbstractList	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 n-qubit gapped local Hamiltonian after learning from only $${{{\mathcal{O}}}(\log (n))$$ O(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $${{{\mathcal{O}}}({n}^{c})$$ O(nc) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $${{{\mathcal{O}}}(n\log n)$$ O(nlogn) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. Recent work proposed a machine learning algorithm for predicting ground state properties of quantum many-body systems that outperforms any non-learning classical algorithm but requires extensive training data. Lewis et al. present an improved algorithm with exponentially reduced training data requirements. 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 n -qubit gapped local Hamiltonian after learning from only O ( log ( n ) ) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require O ( n c ) data for a large constant c . Furthermore, the training and prediction time of the proposed ML model scale as O ( n log n ) in the number of qubits n . Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. Recent work proposed a machine learning algorithm for predicting ground state properties of quantum many-body systems that outperforms any non-learning classical algorithm but requires extensive training data. Lewis et al. present an improved algorithm with exponentially reduced training data requirements. 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 n-qubit gapped local Hamiltonian after learning from only [Formula: see text] data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require [Formula: see text] data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as [Formula: see text] in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. 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 n -qubit gapped local Hamiltonian after learning from only $${{{\mathcal{O}}}(\log (n))$$ O ( log ( n ) ) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $${{{\mathcal{O}}}({n}^{c})$$ O ( n c ) data for a large constant c . Furthermore, the training and prediction time of the proposed ML model scale as $${{{\mathcal{O}}}(n\log n)$$ O ( n log n ) in the number of qubits n . Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. 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 n-qubit gapped local Hamiltonian after learning from only [Formula: see text] data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require [Formula: see text] data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as [Formula: see text] in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.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 n-qubit gapped local Hamiltonian after learning from only [Formula: see text] data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require [Formula: see text] data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as [Formula: see text] in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. 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 n-qubit gapped local Hamiltonian after learning from only $\mathcal{O}$(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $\mathcal{O}$(nc) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $\mathcal{O}$(n log n) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. Abstract 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 n-qubit gapped local Hamiltonian after learning from only $${{{\mathcal{O}}}(\log (n))$$ O ( log ( n ) ) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require $${{{\mathcal{O}}}({n}^{c})$$ O ( n c ) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as $${{{\mathcal{O}}}(n\log n)$$ O ( n log n ) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset. 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 n-qubit gapped local Hamiltonian after learning from only O(log(n)) data about other Hamiltonians in the same quantum phase of matter. This improves substantially upon previous results that require O(nc) data for a large constant c. Furthermore, the training and prediction time of the proposed ML model scale as O(nlogn) in the number of qubits n. Numerical experiments on physical systems with up to 45 qubits confirm the favorable scaling in predicting ground state properties using a small training dataset.Recent work proposed a machine learning algorithm for predicting ground state properties of quantum many-body systems that outperforms any non-learning classical algorithm but requires extensive training data. Lewis et al. present an improved algorithm with exponentially reduced training data requirements.
ArticleNumber	895
Author	Lehner, Sebastian Tran, Viet T. Kueng, Richard Preskill, John Huang, Hsin-Yuan Lewis, Laura
Author_xml	– sequence: 1 givenname: Laura surname: Lewis fullname: Lewis, Laura organization: California Institute of Technology, University of Cambridge – sequence: 2 givenname: Hsin-Yuan orcidid: 0000-0001-5317-2613 surname: Huang fullname: Huang, Hsin-Yuan email: hsinyuan@caltech.edu organization: California Institute of Technology, Massachusetts Institute of Technology, Google Quantum AI – sequence: 3 givenname: Viet T. surname: Tran fullname: Tran, Viet T. organization: Johannes Kepler University – sequence: 4 givenname: Sebastian surname: Lehner fullname: Lehner, Sebastian organization: Johannes Kepler University – sequence: 5 givenname: Richard surname: Kueng fullname: Kueng, Richard organization: Johannes Kepler University – sequence: 6 givenname: John surname: Preskill fullname: Preskill, John organization: California Institute of Technology, AWS Center for Quantum Computing
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Cites_doi	10.1103/PhysRevB.96.195145 10.1137/080734479 10.1007/s00220-011-1380-0 10.1038/s41567-020-0932-7 10.1103/PhysRevB.59.R14157 10.1103/PhysRevB.48.10345 10.1021/acs.jctc.7b00577 10.1038/s41567-019-0512-x 10.21468/SciPostPhys.11.2.025 10.1103/PhysRevLett.104.030502 10.1137/0907087 10.1126/science.231.4738.555 10.1038/s41567-018-0048-5 10.1137/050626090 10.1007/BF00994018 10.1103/PhysRevB.94.195105 10.1126/science.abn7293 10.1126/science.aag2302 10.1038/s41467-019-12875-2 10.1103/PhysRevE.102.033301 10.1016/j.aop.2010.09.012 10.1126/science.abk3333 10.1038/nphys4037 10.1103/RevModPhys.71.1253 10.1103/PhysRevLett.69.2863 10.1038/nphys4035 10.1063/5.0021955 10.1214/009053604000000067 10.1103/RevModPhys.91.045002 10.1103/PhysRevLett.108.058301 10.1038/nature17658 10.1103/PhysRevB.96.205152 10.1103/PhysRevLett.121.255702 10.1103/PhysRevA.75.032321 10.1002/cpa.20042 10.1103/PhysRev.136.B864 10.1038/s41467-020-15724-9 10.1103/PhysRevLett.126.190505 10.1103/PhysRevB.72.045141 10.1103/PhysRevB.94.165134 10.1103/PhysRevLett.99.070502 10.1103/PhysRevLett.125.076402 10.18637/jss.v033.i01 10.1021/acs.jpca.2c05904 10.1103/PhysRevLett.125.200501 10.1103/RevModPhys.90.015002 10.1103/PhysRevA.96.042113 10.1103/PhysRevA.65.012322 10.1038/s42254-022-00535-2 10.48550/arXiv.2204.13691 10.1017/9781316417041 10.48550/arXiv.2212.06084 10.5281/zenodo.10154894 10.1038/s41534-023-00801-w 10.1007/s00220-023-04844-0 10.1103/PRXQuantum.4.040337 10.1038/s41467-021-27922-0 10.48550/arXiv.2204.10268 10.48550/arXiv.2211.16943 10.48550/arXiv.quant-ph/0001106 10.48550/arXiv.2004.04164 10.1111/j.2517-6161.1996.tb02080.x 10.48550/arXiv.2301.12946 10.1038/s41467-020-20314-w 10.1109/CVPR.2009.5206848 10.1109/FOCS52979.2021.00063
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References	Qiao, Welborn, Anandkumar, Manby, Miller III (CR24) 2020; 153 Huang, Rubenstein (CR33) 2022; 127 Rodriguez-Nieva, Scheurer (CR23) 2019; 15 Daubechies, Defrise, De Mol (CR54) 2004; 57 Glasser, Pancotti, August, Rodriguez, Cirac (CR21) 2018; 8 CR37 White (CR7) 1993; 48 Deng, Li, Das Sarma (CR10) 2017; 96 Du (CR43) 2010; 104 Osborne (CR80) 2007; 75 van Nieuwenburg, Liu, Huber (CR15) 2017; 13 Torlai (CR18) 2018; 14 CR75 CR30 CR74 CR73 CR72 CR71 Carleo, Troyer (CR12) 2017; 355 Huang, Kueng, Preskill (CR76) 2021; 126 Carrasquilla, Melko (CR11) 2017; 13 Wang (CR16) 2016; 94 Huang (CR77) 2022; 376 White (CR64) 1992; 69 Tibshirani (CR46) 1996; 58 CR5 Childs, Farhi, Preskill (CR39) 2001; 65 Vargas-Hernández, Sous, Berciu, Krems (CR19) 2018; 121 Hohenberg, Kohn (CR1) 1964; 136 CR47 Ceperley, Alder (CR3) 1986; 231 CR44 Kohn (CR2) 1999; 71 White (CR6) 1992; 69 Efron, Hastie, Johnstone, Tibshirani (CR53) 2004; 32 CR83 Moreno, Carleo, Georges (CR27) 2020; 125 CR82 CR81 Huang, Kueng, Torlai, Albert, Preskill (CR36) 2022; 377 Hastings, Wen (CR79) 2005; 72 Liu, Shao, Lin, Guo, W Sandvik (CR63) 2018; 8 Schütt, Gastegger, Tkatchenko, Müller, Maurer (CR20) 2019; 10 Faber (CR35) 2017; 13 Mizel, Lidar, Mitchell (CR38) 2007; 99 Schollwoeck (CR65) 2011; 326 Sandvik (CR4) 1999; 59 Barends (CR41) 2016; 534 Huang (CR61) 2021; 12 CR17 CR59 CR58 Albash, Lidar (CR42) 2018; 90 CR56 Cortes, Vapnik (CR66) 1995; 20 Nomura, Darmawan, Yamaji, Imada (CR14) 2017; 96 CR52 Torlai, Melko (CR13) 2016; 94 CR51 CR50 Brown (CR70) 2020; 33 Carrasquilla (CR9) 2020; 5 Combettes, Wajs (CR55) 2005; 4 Elben (CR49) 2020; 125 Huang, Kueng, Preskill (CR48) 2020; 16 Bachmann, Michalakis, Nachtergaele, Sims (CR78) 2012; 309 CR29 Santosa, Symes (CR45) 1986; 7 CR69 Kawai, Nakagawa (CR26) 2020; 1 CR68 CR67 Carleo (CR8) 2019; 91 CR22 Kottmann, Corboz, Lewenstein, Acín (CR28) 2021; 11 Choo, Mezzacapo, Carleo (CR25) 2020; 11 Rupp, Tkatchenko, Müller, Von Lilienfeld (CR34) 2012; 108 Mills, Spanner, Tamblyn (CR31) 2017; 96 CR62 CR60 Saraceni, Cantori, Pilati (CR32) 2020; 102 Friedman, Hastie, Tibshirani (CR57) 2010; 33 Aharonov (CR40) 2008; 50 45014_CR71 PL Combettes (45014_CR55) 2005; 4 45014_CR72 L Liu (45014_CR63) 2018; 8 J Du (45014_CR43) 2010; 104 SR White (45014_CR64) 1992; 69 45014_CR75 A Mizel (45014_CR38) 2007; 99 AM Childs (45014_CR39) 2001; 65 45014_CR73 45014_CR30 45014_CR74 I Daubechies (45014_CR54) 2004; 57 Y Nomura (45014_CR14) 2017; 96 JF Rodriguez-Nieva (45014_CR23) 2019; 15 45014_CR29 MB Hastings (45014_CR79) 2005; 72 G Torlai (45014_CR18) 2018; 14 D Ceperley (45014_CR3) 1986; 231 Hsin-Yuan Huang (45014_CR77) 2022; 376 Hsin-Yuan Huang (45014_CR48) 2020; 16 B Efron (45014_CR53) 2004; 32 P Hohenberg (45014_CR1) 1964; 136 T Albash (45014_CR42) 2018; 90 45014_CR60 45014_CR68 45014_CR69 45014_CR22 45014_CR67 45014_CR62 J Carrasquilla (45014_CR9) 2020; 5 RA Vargas-Hernández (45014_CR19) 2018; 121 J Carrasquilla (45014_CR11) 2017; 13 K Mills (45014_CR31) 2017; 96 45014_CR17 C Huang (45014_CR33) 2022; 127 45014_CR59 EvertPL van Nieuwenburg (45014_CR15) 2017; 13 JavierRobledo Moreno (45014_CR27) 2020; 125 T Brown (45014_CR70) 2020; 33 Z Qiao (45014_CR24) 2020; 153 G Carleo (45014_CR12) 2017; 355 Dong-Ling Deng (45014_CR10) 2017; 96 R Barends (45014_CR41) 2016; 534 45014_CR50 A Elben (45014_CR49) 2020; 125 C Cortes (45014_CR66) 1995; 20 Hsin-Yuan Huang (45014_CR36) 2022; 377 G Torlai (45014_CR13) 2016; 94 L Wang (45014_CR16) 2016; 94 SR White (45014_CR7) 1993; 48 45014_CR58 G Carleo (45014_CR8) 2019; 91 F Santosa (45014_CR45) 1986; 7 45014_CR56 H Kawai (45014_CR26) 2020; 1 K Kottmann (45014_CR28) 2021; 11 KT Schütt (45014_CR20) 2019; 10 M Rupp (45014_CR34) 2012; 108 45014_CR51 45014_CR5 45014_CR52 Hsin-Yuan Huang (45014_CR76) 2021; 126 H-Y Huang (45014_CR61) 2021; 12 S Bachmann (45014_CR78) 2012; 309 SR White (45014_CR6) 1992; 69 N Saraceni (45014_CR32) 2020; 102 TJ Osborne (45014_CR80) 2007; 75 45014_CR82 45014_CR83 45014_CR81 AW Sandvik (45014_CR4) 1999; 59 W Kohn (45014_CR2) 1999; 71 45014_CR47 J Friedman (45014_CR57) 2010; 33 45014_CR44 FA Faber (45014_CR35) 2017; 13 K Choo (45014_CR25) 2020; 11 I Glasser (45014_CR21) 2018; 8 D Aharonov (45014_CR40) 2008; 50 45014_CR37 R Tibshirani (45014_CR46) 1996; 58 U Schollwoeck (45014_CR65) 2011; 326
References_xml	– volume: 96 start-page: 195145 year: 2017 ident: CR10 article-title: Machine learning topological states publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.96.195145 – ident: CR22 – volume: 50 start-page: 755 year: 2008 end-page: 787 ident: CR40 article-title: Adiabatic quantum computation is equivalent to standard quantum computation publication-title: SIAM Rev. doi: 10.1137/080734479 – volume: 309 start-page: 835 year: 2012 end-page: 871 ident: CR78 article-title: Automorphic equivalence within gapped phases of quantum lattice systems publication-title: Commun. Math. Phys. doi: 10.1007/s00220-011-1380-0 – ident: CR68 – ident: CR74 – volume: 16 start-page: 1050 year: 2020 end-page: 1057 ident: CR48 article-title: Predicting many properties of a quantum system from very few measurements publication-title: Nat. Phys. doi: 10.1038/s41567-020-0932-7 – volume: 59 start-page: R14157 year: 1999 end-page: R14160 ident: CR4 article-title: Stochastic series expansion method with operator-loop update publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.59.R14157 – ident: CR51 – volume: 48 start-page: 10345 year: 1993 end-page: 10356 ident: CR7 article-title: Density-matrix algorithms for quantum renormalization groups publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.48.10345 – volume: 13 start-page: 5255 year: 2017 end-page: 5264 ident: CR35 article-title: Prediction errors of molecular machine learning models lower than hybrid dft error publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.7b00577 – volume: 8 start-page: 041040 year: 2018 ident: CR63 article-title: Random-singlet phase in disordered two-dimensional quantum magnets publication-title: Phys. Rev. X – volume: 8 start-page: 011006 year: 2018 ident: CR21 article-title: Neural-network quantum states, string-bond states, and chiral topological states publication-title: Phys. Rev. X – ident: CR29 – volume: 15 start-page: 790 year: 2019 end-page: 795 ident: CR23 article-title: Identifying topological order through unsupervised machine learning publication-title: Nat. Phys. doi: 10.1038/s41567-019-0512-x – ident: CR58 – volume: 11 start-page: 025 year: 2021 ident: CR28 article-title: Unsupervised mapping of phase diagrams of 2d systems from infinite projected entangled-pair states via deep anomaly detection publication-title: SciPost Phys. doi: 10.21468/SciPostPhys.11.2.025 – volume: 104 start-page: 030502 year: 2010 ident: CR43 article-title: Nmr implementation of a molecular hydrogen quantum simulation with adiabatic state preparation publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.104.030502 – volume: 58 start-page: 267 year: 1996 end-page: 288 ident: CR46 article-title: Regression shrinkage and selection via the lasso publication-title: J. R. Stat. Soc. – ident: CR71 – volume: 7 start-page: 1307 year: 1986 end-page: 1330 ident: CR45 article-title: Linear inversion of band-limited reflection seismograms publication-title: SIAM J. Sci. Stat. Comput. doi: 10.1137/0907087 – volume: 231 start-page: 555 year: 1986 end-page: 560 ident: CR3 article-title: Quantum Monte Carlo publication-title: Science doi: 10.1126/science.231.4738.555 – ident: CR67 – ident: CR75 – ident: CR50 – volume: 14 start-page: 447 year: 2018 end-page: 450 ident: CR18 article-title: Neural-network quantum state tomography publication-title: Nat. Phys. doi: 10.1038/s41567-018-0048-5 – volume: 4 start-page: 1168 year: 2005 end-page: 1200 ident: CR55 article-title: Signal recovery by proximal forward-backward splitting publication-title: Multiscale Model. Simul. doi: 10.1137/050626090 – volume: 20 start-page: 273 year: 1995 end-page: 297 ident: CR66 article-title: Support-vector networks publication-title: Mach. Learn. doi: 10.1007/BF00994018 – ident: CR60 – ident: CR5 – ident: CR81 – volume: 94 start-page: 195105 year: 2016 ident: CR16 article-title: Discovering phase transitions with unsupervised learning publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.94.195105 – volume: 376 start-page: 1182 year: 2022 end-page: 1186 ident: CR77 article-title: Quantum advantage in learning from experiments publication-title: Science doi: 10.1126/science.abn7293 – volume: 355 start-page: 602 year: 2017 end-page: 606 ident: CR12 article-title: Solving the quantum many-body problem with artificial neural networks publication-title: Science doi: 10.1126/science.aag2302 – volume: 10 start-page: 1 year: 2019 end-page: 10 ident: CR20 article-title: Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions publication-title: Nat. Commun. doi: 10.1038/s41467-019-12875-2 – volume: 102 start-page: 033301 year: 2020 ident: CR32 article-title: Scalable neural networks for the efficient learning of disordered quantum systems publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.102.033301 – volume: 326 start-page: 96 year: 2011 end-page: 192 ident: CR65 article-title: The density-matrix renormalization group in the age of matrix product states publication-title: Ann. Phys. doi: 10.1016/j.aop.2010.09.012 – volume: 377 start-page: eabk3333 year: 2022 ident: CR36 article-title: Provably efficient machine learning for quantum many-body problems publication-title: Science doi: 10.1126/science.abk3333 – volume: 13 start-page: 435 year: 2017 ident: CR15 article-title: Learning phase transitions by confusion publication-title: Nat. Phys. doi: 10.1038/nphys4037 – ident: CR47 – ident: CR72 – volume: 71 start-page: 1253 year: 1999 end-page: 1266 ident: CR2 article-title: Nobel lecture: Electronic structure of matter—wave functions and density functionals publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.71.1253 – volume: 69 start-page: 2863 year: 1992 end-page: 2866 ident: CR6 article-title: Density matrix formulation for quantum renormalization groups publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.69.2863 – volume: 1 start-page: 045027 year: 2020 ident: CR26 article-title: Predicting excited states from ground state wavefunction by supervised quantum machine learning publication-title: Mach. Learn. – ident: CR37 – volume: 13 start-page: 431 year: 2017 ident: CR11 article-title: Machine learning phases of matter publication-title: Nat. Phys. doi: 10.1038/nphys4035 – volume: 153 start-page: 124111 year: 2020 ident: CR24 article-title: Orbnet: deep learning for quantum chemistry using symmetry-adapted atomic-orbital features publication-title: J. Chem. Phys. doi: 10.1063/5.0021955 – volume: 32 start-page: 407 year: 2004 end-page: 499 ident: CR53 article-title: Least angle regression publication-title: Ann. Stat. doi: 10.1214/009053604000000067 – volume: 91 start-page: 045002 year: 2019 ident: CR8 article-title: Machine learning and the physical sciences publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.91.045002 – ident: CR30 – volume: 108 start-page: 058301 year: 2012 ident: CR34 article-title: Fast and accurate modeling of molecular atomization energies with machine learning publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.108.058301 – volume: 534 start-page: 222 year: 2016 end-page: 226 ident: CR41 article-title: Digitized adiabatic quantum computing with a superconducting circuit publication-title: Nature doi: 10.1038/nature17658 – volume: 96 start-page: 205152 year: 2017 ident: CR14 article-title: Restricted boltzmann machine learning for solving strongly correlated quantum systems publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.96.205152 – ident: CR82 – volume: 121 start-page: 255702 year: 2018 ident: CR19 article-title: Extrapolating quantum observables with machine learning: inferring multiple phase transitions from properties of a single phase publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.121.255702 – volume: 75 start-page: 032321 year: 2007 ident: CR80 article-title: Simulating adiabatic evolution of gapped spin systems publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.75.032321 – volume: 33 start-page: 1877 year: 2020 end-page: 1901 ident: CR70 article-title: Language models are few-shot learners publication-title: Adv. Neural Inf. Process. Syst. – volume: 57 start-page: 1413 year: 2004 end-page: 1457 ident: CR54 article-title: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint publication-title: Commun. Pure Appl. Math. doi: 10.1002/cpa.20042 – ident: CR56 – volume: 136 start-page: B864 year: 1964 end-page: B871 ident: CR1 article-title: Inhomogeneous electron gas publication-title: Phys. Rev. doi: 10.1103/PhysRev.136.B864 – volume: 11 year: 2020 ident: CR25 article-title: Fermionic neural-network states for ab-initio electronic structure publication-title: Nat. Commun. doi: 10.1038/s41467-020-15724-9 – volume: 126 start-page: 190505 year: 2021 ident: CR76 article-title: Information-theoretic bounds on quantum advantage in machine learning publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.126.190505 – volume: 72 start-page: 045141 year: 2005 ident: CR79 article-title: Quasiadiabatic continuation of quantum states: the stability of topological ground-state degeneracy and emergent gauge invariance publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.72.045141 – volume: 94 start-page: 165134 year: 2016 ident: CR13 article-title: Learning thermodynamics with Boltzmann machines publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.94.165134 – ident: CR69 – volume: 99 start-page: 070502 year: 2007 ident: CR38 article-title: Simple proof of equivalence between adiabatic quantum computation and the circuit model publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.99.070502 – ident: CR44 – volume: 125 start-page: 076402 year: 2020 ident: CR27 article-title: Deep learning the hohenberg-kohn maps of density functional theory publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.125.076402 – volume: 33 start-page: 1 year: 2010 ident: CR57 article-title: Regularization paths for generalized linear models via coordinate descent publication-title: J. Stat. Softw. doi: 10.18637/jss.v033.i01 – ident: CR73 – volume: 69 start-page: 2863 year: 1992 ident: CR64 article-title: Density matrix formulation for quantum renormalization groups publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.69.2863 – volume: 127 start-page: 339 year: 2022 end-page: 355 ident: CR33 article-title: Machine learning diffusion monte carlo forces publication-title: J. Phys. Chem. A doi: 10.1021/acs.jpca.2c05904 – volume: 125 start-page: 200501 year: 2020 ident: CR49 article-title: Mixed-state entanglement from local randomized measurements publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.125.200501 – ident: CR52 – ident: CR17 – volume: 90 start-page: 015002 year: 2018 ident: CR42 article-title: Adiabatic quantum computation publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.90.015002 – volume: 5 start-page: 1797528 year: 2020 ident: CR9 article-title: Machine learning for quantum matter publication-title: Adv. Phys. X – volume: 12 start-page: 1 year: 2021 end-page: 9 ident: CR61 article-title: Power of data in quantum machine learning publication-title: Nat. Commun. – ident: CR59 – ident: CR83 – volume: 96 start-page: 042113 issue: Oct year: 2017 ident: CR31 article-title: Deep learning and the schrödinger equation publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.96.042113 – ident: CR62 – volume: 65 start-page: 012322 year: 2001 ident: CR39 article-title: Robustness of adiabatic quantum computation publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.65.012322 – ident: 45014_CR50 doi: 10.1038/s42254-022-00535-2 – volume: 90 start-page: 015002 year: 2018 ident: 45014_CR42 publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.90.015002 – volume: 91 start-page: 045002 year: 2019 ident: 45014_CR8 publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.91.045002 – volume: 13 start-page: 5255 year: 2017 ident: 45014_CR35 publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.7b00577 – ident: 45014_CR75 doi: 10.48550/arXiv.2204.13691 – ident: 45014_CR5 doi: 10.1017/9781316417041 – volume: 127 start-page: 339 year: 2022 ident: 45014_CR33 publication-title: J. Phys. Chem. A doi: 10.1021/acs.jpca.2c05904 – volume: 94 start-page: 165134 year: 2016 ident: 45014_CR13 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.94.165134 – volume: 14 start-page: 447 year: 2018 ident: 45014_CR18 publication-title: Nat. Phys. doi: 10.1038/s41567-018-0048-5 – ident: 45014_CR60 doi: 10.48550/arXiv.2212.06084 – volume: 72 start-page: 045141 year: 2005 ident: 45014_CR79 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.72.045141 – ident: 45014_CR58 – volume: 75 start-page: 032321 year: 2007 ident: 45014_CR80 publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.75.032321 – ident: 45014_CR83 doi: 10.5281/zenodo.10154894 – volume: 534 start-page: 222 year: 2016 ident: 45014_CR41 publication-title: Nature doi: 10.1038/nature17658 – volume: 125 start-page: 076402 year: 2020 ident: 45014_CR27 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.125.076402 – volume: 59 start-page: R14157 year: 1999 ident: 45014_CR4 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.59.R14157 – volume: 104 start-page: 030502 year: 2010 ident: 45014_CR43 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.104.030502 – ident: 45014_CR67 – volume: 7 start-page: 1307 year: 1986 ident: 45014_CR45 publication-title: SIAM J. Sci. Stat. Comput. doi: 10.1137/0907087 – ident: 45014_CR52 doi: 10.1038/s41534-023-00801-w – volume: 355 start-page: 602 year: 2017 ident: 45014_CR12 publication-title: Science doi: 10.1126/science.aag2302 – ident: 45014_CR51 doi: 10.1007/s00220-023-04844-0 – ident: 45014_CR47 – volume: 99 start-page: 070502 year: 2007 ident: 45014_CR38 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.99.070502 – volume: 8 start-page: 041040 year: 2018 ident: 45014_CR63 publication-title: Phys. Rev. X – ident: 45014_CR68 – ident: 45014_CR81 doi: 10.1103/PRXQuantum.4.040337 – volume: 153 start-page: 124111 year: 2020 ident: 45014_CR24 publication-title: J. Chem. Phys. doi: 10.1063/5.0021955 – volume: 96 start-page: 042113 issue: Oct year: 2017 ident: 45014_CR31 publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.96.042113 – volume: 125 start-page: 200501 year: 2020 ident: 45014_CR49 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.125.200501 – volume: 11 year: 2020 ident: 45014_CR25 publication-title: Nat. Commun. doi: 10.1038/s41467-020-15724-9 – volume: 16 start-page: 1050 year: 2020 ident: 45014_CR48 publication-title: Nat. Phys. doi: 10.1038/s41567-020-0932-7 – ident: 45014_CR73 doi: 10.1038/s41467-021-27922-0 – ident: 45014_CR22 doi: 10.48550/arXiv.2204.10268 – volume: 71 start-page: 1253 year: 1999 ident: 45014_CR2 publication-title: Rev. Mod. Phys. doi: 10.1103/RevModPhys.71.1253 – ident: 45014_CR29 doi: 10.48550/arXiv.2211.16943 – volume: 13 start-page: 431 year: 2017 ident: 45014_CR11 publication-title: Nat. Phys. doi: 10.1038/nphys4035 – ident: 45014_CR37 doi: 10.48550/arXiv.quant-ph/0001106 – ident: 45014_CR44 doi: 10.48550/arXiv.2004.04164 – volume: 69 start-page: 2863 year: 1992 ident: 45014_CR6 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.69.2863 – volume: 33 start-page: 1 year: 2010 ident: 45014_CR57 publication-title: J. Stat. Softw. doi: 10.18637/jss.v033.i01 – volume: 32 start-page: 407 year: 2004 ident: 45014_CR53 publication-title: Ann. Stat. doi: 10.1214/009053604000000067 – volume: 48 start-page: 10345 year: 1993 ident: 45014_CR7 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.48.10345 – volume: 4 start-page: 1168 year: 2005 ident: 45014_CR55 publication-title: Multiscale Model. Simul. doi: 10.1137/050626090 – volume: 15 start-page: 790 year: 2019 ident: 45014_CR23 publication-title: Nat. Phys. doi: 10.1038/s41567-019-0512-x – volume: 94 start-page: 195105 year: 2016 ident: 45014_CR16 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.94.195105 – ident: 45014_CR69 – volume: 58 start-page: 267 year: 1996 ident: 45014_CR46 publication-title: J. R. Stat. Soc. doi: 10.1111/j.2517-6161.1996.tb02080.x – ident: 45014_CR17 – volume: 20 start-page: 273 year: 1995 ident: 45014_CR66 publication-title: Mach. Learn. doi: 10.1007/BF00994018 – ident: 45014_CR56 – volume: 309 start-page: 835 year: 2012 ident: 45014_CR78 publication-title: Commun. Math. Phys. doi: 10.1007/s00220-011-1380-0 – volume: 96 start-page: 195145 year: 2017 ident: 45014_CR10 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.96.195145 – volume: 126 start-page: 190505 year: 2021 ident: 45014_CR76 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.126.190505 – ident: 45014_CR82 doi: 10.48550/arXiv.2301.12946 – volume: 33 start-page: 1877 year: 2020 ident: 45014_CR70 publication-title: Adv. Neural Inf. Process. Syst. – volume: 231 start-page: 555 year: 1986 ident: 45014_CR3 publication-title: Science doi: 10.1126/science.231.4738.555 – volume: 13 start-page: 435 year: 2017 ident: 45014_CR15 publication-title: Nat. Phys. doi: 10.1038/nphys4037 – volume: 121 start-page: 255702 year: 2018 ident: 45014_CR19 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.121.255702 – volume: 326 start-page: 96 year: 2011 ident: 45014_CR65 publication-title: Ann. Phys. doi: 10.1016/j.aop.2010.09.012 – volume: 5 start-page: 1797528 year: 2020 ident: 45014_CR9 publication-title: Adv. Phys. X – volume: 136 start-page: B864 year: 1964 ident: 45014_CR1 publication-title: Phys. Rev. doi: 10.1103/PhysRev.136.B864 – volume: 57 start-page: 1413 year: 2004 ident: 45014_CR54 publication-title: Commun. Pure Appl. Math. doi: 10.1002/cpa.20042 – volume: 12 start-page: 1 year: 2021 ident: 45014_CR61 publication-title: Nat. Commun. doi: 10.1038/s41467-020-20314-w – volume: 10 start-page: 1 year: 2019 ident: 45014_CR20 publication-title: Nat. Commun. doi: 10.1038/s41467-019-12875-2 – ident: 45014_CR72 – volume: 8 start-page: 011006 year: 2018 ident: 45014_CR21 publication-title: Phys. Rev. X – ident: 45014_CR30 – volume: 96 start-page: 205152 year: 2017 ident: 45014_CR14 publication-title: Phys. Rev. B doi: 10.1103/PhysRevB.96.205152 – volume: 108 start-page: 058301 year: 2012 ident: 45014_CR34 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.108.058301 – volume: 50 start-page: 755 year: 2008 ident: 45014_CR40 publication-title: SIAM Rev. doi: 10.1137/080734479 – volume: 11 start-page: 025 year: 2021 ident: 45014_CR28 publication-title: SciPost Phys. doi: 10.21468/SciPostPhys.11.2.025 – volume: 102 start-page: 033301 year: 2020 ident: 45014_CR32 publication-title: Phys. Rev. E doi: 10.1103/PhysRevE.102.033301 – volume: 376 start-page: 1182 year: 2022 ident: 45014_CR77 publication-title: Science doi: 10.1126/science.abn7293 – volume: 65 start-page: 012322 year: 2001 ident: 45014_CR39 publication-title: Phys. Rev. A doi: 10.1103/PhysRevA.65.012322 – ident: 45014_CR59 – volume: 377 start-page: eabk3333 year: 2022 ident: 45014_CR36 publication-title: Science doi: 10.1126/science.abk3333 – ident: 45014_CR62 – ident: 45014_CR71 doi: 10.1109/CVPR.2009.5206848 – ident: 45014_CR74 doi: 10.1109/FOCS52979.2021.00063 – volume: 1 start-page: 045027 year: 2020 ident: 45014_CR26 publication-title: Mach. Learn. – volume: 69 start-page: 2863 year: 1992 ident: 45014_CR64 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.69.2863
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Snippet	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)... Abstract 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...
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SubjectTerms	639/705/117 639/766/483/1139 639/766/483/481 Algorithms CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS Computer science Ground state Humanities and Social Sciences Learning algorithms Machine learning MATHEMATICS AND COMPUTING multidisciplinary Quantum information Quantum mechanics Quantum theory Qubits (quantum computing) Science Science & Technology Science (multidisciplinary)
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Title	Improved machine learning algorithm for predicting ground state properties
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Volume	15
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