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Efficient simulation of low-temperature physics in one-dimensional gapless systems
Yuya Kusuki, Kotaro Tamaoka, Zixia Wei, and Yasushi Yoneta
Phys. Rev. B 110, L041122 – Published 29 July 2024
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Abstract
We discuss the computational efficiency of the finite-temperature simulation with minimally entangled typical thermal states (METTS). To argue that METTS can be efficiently represented as matrix product states, we present an analytic upper bound for the average entanglement Rényi entropy of METTS for a Rényi index . In particular, for one-dimensional (1D) gapless systems described by conformal field theories, the upper bound scales as where is the central charge and is the system size. Furthermore, we numerically find that the average Rényi entropy exhibits a universal behavior characterized by the central charge and is roughly given by half of the analytic upper bound. Based on these results, we show that METTS can provide a speedup compared to employing the purification method to analyze thermal equilibrium states at low temperatures in 1D gapless systems.
- Received 23 February 2024
- Revised 28 May 2024
- Accepted 24 June 2024
DOI:https://doi.org/10.1103/PhysRevB.110.L041122
©2024 American Physical Society
Physics Subject Headings (PhySH)
- Research Areas
Entanglement in field theoryQuantum entanglementQuantum statistical mechanics
- Techniques
Conformal field theoryDensity matrix renormalization groupTensor network methods
Condensed Matter, Materials & Applied PhysicsStatistical Physics & ThermodynamicsQuantum Information, Science & TechnologyParticles & Fields
Authors & Affiliations
Yuya Kusuki1,2, Kotaro Tamaoka3, Zixia Wei4,2,5,*, and Yasushi Yoneta6,7
- 1Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena, California 91125, USA
- 2Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS), RIKEN, Wako, Saitama 351-0198, Japan
- 3Department of Physics, College of Humanities and Sciences, Nihon University, Tokyo 156-8550, Japan
- 4Center for the Fundamental Laws of Nature and Society of Fellows, Harvard University, Cambridge, Massachusetts 02138, USA
- 5Yukawa Institute for Theoretical Physics, Kyoto University, Sakyo-ku, Kyoto 606-8502, Japan
- 6Center for Quantum Computing, RIKEN, Wako, Saitama 351-0198, Japan
- 7Department of Basic Science, The University of Tokyo, Meguro, Tokyo 153-8902, Japan
- *Contact author: zixiawei@fas.harvard.edu
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Issue
Vol. 110, Iss. 4 — 15 July 2024
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Article part of CHORUS
Accepted manuscript will be available starting29 July 2025.Images
Figure 1
Average entanglement entropy of METTS between the left part (sites ) and the right part (sites ) as a function of the size of the subsystem for the critical transverse-field Ising chain with and . Imaginary-time evolution is carried out using second-order Trotter decomposition with a time step . The data are averaged over samples.
Figure 2
Growth of the average entanglement Rényi entropy of METTS for the half chain (a), (b)in the critical transverse-field Ising model of length and (c)in the critical Heisenberg model with the next-nearest-neighbor interaction of length . The insets show the standard deviation of the entanglement Rényi entropy. Imaginary-time evolution is carried out using second-order Trotter decomposition with a time step for the Ising model and for the Heisenberg model. The data are averaged over samples for the Ising model and 5000 samples for the Heisenberg model.
Figure 3
Minimum bond dimension necessary to approximate the TFD state and METTS with a truncation error of less than for the critical transverse-field Ising chain with sites. The data points for METTS are the average of of samples. The error bars indicate the standard deviation. We set the quantization axis to . Imaginary-time evolution is carried out using second-order Trotter decomposition with a time step .
Figure 4
Typical trajectory of the cumulative moving average of the energy per site in the Markov chain of the METTS simulation for the critical transverse-field Ising chain with sites at . We set the quantization axis to for the odd-numbered steps and for the even-numbered steps to reduce correlations between successive samples [22]. To avoid initial transients, we discard the first five samples when calculating the sample averages. The solid line shows the result of the TFD approach, considered exact, and the shaded region shows a range of 0.1 times the standard deviation of in from the exact value. The inset shows the total computation time in the METTS simulation as a function of the number of steps in the Markov chain. The solid line shows the computation time in the TFD simulation.