Publications

Order by: publication date number of citations

• Codebase release 1.5 for PauliStrings.jl

  Nicolas Loizeau, J. Clayton Peacock, Dries Sels

  SciPost Phys. Codebases 54-r1.5 (2025) · published 24 March 2025 | Toggle abstract · src

  We present the Julia package PauliStrings.jl for quantum many-body simulations, which performs fast operations on the Pauli group by encoding Pauli strings in binary. All of the Pauli string algebra is encoded into low-level logic operations on integers, and is made efficient by various truncation methods which allow for systematic extrapolation of the results. We illustrate the effectiveness of our package by (i) performing Heisenberg time evolution through direct numerical integration and (ii) by constructing a Liouvillian Krylov space. We benchmark the results against tensor network methods, and we find our package performs favorably. In addition, we show that this representation allows for easy encoding of any geometry. We present results for chaotic and integrable spin systems in 1D as well as some examples in 2D. Currently, the main limitations are the inefficiency of representing non-trivial pure states (or other low-rank operators), as well as the need to introduce dissipation to probe long-time dynamics.

  1 citation

• Learning tensor networks with tensor cross interpolation: New algorithms and libraries

  Yuriel Núñez Fernández, Marc K. Ritter, Matthieu Jeannin, Jheng-Wei Li, Thomas Kloss, Thibaud Louvet, Satoshi Terasaki, Olivier Parcollet, Jan von Delft, Hiroshi Shinaoka, Xavier Waintal

  SciPost Phys. 18, 104 (2025) · published 20 March 2025 | Toggle abstract · pdf

  The tensor cross interpolation (TCI) algorithm is a rank-revealing algorithm for decomposing low-rank, high-dimensional tensors into tensor trains/matrix product states (MPS). TCI learns a compact MPS representation of the entire object from a tiny training data set. Once obtained, the large existing MPS toolbox provides exponentially fast algorithms for performing a large set of operations. We discuss several improvements and variants of TCI. In particular, we show that replacing the cross interpolation by the partially rank-revealing LU decomposition yields a more stable and more flexible algorithm than the original algorithm. We also present two open source libraries, xfac in Python/C++ and TensorCrossInterpolation.jl in Julia, that implement these improved algorithms, and illustrate them on several applications. These include sign-problem-free integration in large dimension, the "superhigh-resolution" quantics representation of functions, the solution of partial differential equations, the superfast Fourier transform, the computation of partition functions, and the construction of matrix product operators.

  3 citations

• Generative learning of continuous data by tensor networks

  Alex Meiburg, Jing Chen, Jacob Miller, Raphaëlle Tihon, Guillaume Rabusseau, Alejandro Perdomo-Ortiz

  SciPost Phys. 18, 096 (2025) · published 17 March 2025 | Toggle abstract · pdf

  Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning. While possessing many desirable features arising from their quantum-inspired nature, tensor network generative models have previously been largely restricted to binary or categorical data, limiting their utility in real-world modeling problems. We overcome this by introducing a new family of tensor network generative models for continuous data, which are capable of learning from distributions containing continuous random variables. We develop our method in the setting of matrix product states, first deriving a universal expressivity theorem proving the ability of this model family to approximate any reasonably smooth probability density function with arbitrary precision. We then benchmark the performance of this model on several synthetic and real-world datasets, finding that the model learns and generalizes well on distributions of continuous and discrete variables. We develop methods for modeling different data domains, and introduce a trainable compression layer which is found to increase model performance given limited memory or computational resources. Overall, our methods give important theoretical and empirical evidence of the efficacy of quantum-inspired methods for the rapidly growing field of generative learning.

• Codebase release 1.0 for Cytnx

  Kai-Hsin Wu, Chang-Teng Lin, Ke Hsu, Hao-Ti Hung, Manuel Schneider, Chia-Min Chung, Ying-Jer Kao, Pochung Chen

  SciPost Phys. Codebases 53-r1.0 (2025) · published 13 March 2025 | Toggle abstract · src

  We introduce a tensor network library designed for classical and quantum physics simulations called Cytnx (pronounced as sci-tens). This library provides an almost identical interface and syntax for both C++ and Python, allowing users to effortlessly switch between the two languages. Aiming at a quick learning process for new users of tensor network algorithms, the interfaces resemble the popular Python scientific libraries like NumPy, Scipy, and PyTorch. Not only multiple global Abelian symmetries can be easily defined and implemented, Cytnx also provides a new tool called Network that allows users to store large tensor networks and perform tensor network contractions in an optimal order automatically. With the integration of cuQuantum, tensor calculations can also be executed efficiently on GPUs. We present benchmark results for tensor operations on both devices, CPU and GPU. We also discuss features and higher-level interfaces to be added in the future.

  1 citation

• The Cytnx library for tensor networks

  Kai-Hsin Wu, Chang-Teng Lin, Ke Hsu, Hao-Ti Hung, Manuel Schneider, Chia-Min Chung, Ying-Jer Kao, Pochung Chen

  SciPost Phys. Codebases 53 (2025) · published 13 March 2025 | Toggle abstract · pdf

  We introduce a tensor network library designed for classical and quantum physics simulations called Cytnx (pronounced as sci-tens). This library provides an almost identical interface and syntax for both C++ and Python, allowing users to effortlessly switch between the two languages. Aiming at a quick learning process for new users of tensor network algorithms, the interfaces resemble the popular Python scientific libraries like NumPy, Scipy, and PyTorch. Not only multiple global Abelian symmetries can be easily defined and implemented, Cytnx also provides a new tool called Network that allows users to store large tensor networks and perform tensor network contractions in an optimal order automatically. With the integration of cuQuantum, tensor calculations can also be executed efficiently on GPUs. We present benchmark results for tensor operations on both devices, CPU and GPU. We also discuss features and higher-level interfaces to be added in the future.

  1 citation

• A large-$N$ approach to magnetic impurities in superconductors

  Chen-How Huang, Alejandro M. Lobos, Miguel A. Cazalilla

  SciPost Phys. 18, 087 (2025) · published 10 March 2025 | Toggle abstract · pdf

  Quantum spin impurities coupled to superconductors are under intense investigation for their relevance to fundamental research as well as the prospects to engineer novel quantum phases of matter. Here we develop a large-$N$ mean-field theory of a strongly coupled spin-$\tfrac{1}{2}$ quantum impurity in a conventional $s$-wave superconductor. The approach is benchmarked against Wilson's numerical renormalization group (NRG). While the large-$N$ method is not applicable in the weak-coupling regime where the Kondo temperature $T_K$ is smaller than the superconducting gap $\Delta$, it performs very well in the strong coupling regime where $T_K \gtrsim \Delta$, thus allowing us to obtain a reasonably accurate description of experimentally relevant quantities. The latter include the energy of the Yu-Shiba-Rusinov subgap states, their spectral weight, as well as the local density of continuum states. The method provides a reliable analytical tool that complements other perturbative and non-perturbative methods, and can be extended to more complex impurity models for which NRG may not be easily applicable.

• Identifying quantum phase transitions with minimal prior knowledge by unsupervised learning

  Mohamad Ali Marashli, Ho Lai Henry Lam, Hamam Mokayed, Fredrik Sandin, Marcus Liwicki, Ho-Kin Tang, Wing Chi Yu

  SciPost Phys. Core 8, 029 (2025) · published 6 March 2025 | Toggle abstract · pdf

  In this work, we proposed a novel approach for identifying quantum phase transitions in one-dimensional quantum many-body systems using AutoEncoder (AE), an unsupervised machine learning technique, with minimal prior knowledge. The training of the AEs is done with reduced density matrix (RDM) data obtained by Exact Diagonalization (ED) across the entire range of the driving parameter and thus no prior knowledge of the phase diagram is required. With this method, we successfully detect the phase transitions in a wide range of models with multiple phase transitions of different types, including the topological and the Berezinskii-Kosterlitz-Thouless transitions by tracking the changes in the reconstruction loss of the AE. The learned representation of the AE is used to characterize the physical phenomena underlying different quantum phases. Our methodology demonstrates a new approach to studying quantum phase transitions with minimal knowledge, small amount of needed data, and produces compressed representations of the quantum states.

• Two-particle self-consistent approach for broken symmetry phases

  Lorenzo Del Re

  SciPost Phys. 18, 077 (2025) · published 3 March 2025 | Toggle abstract · pdf

  Spontaneous symmetry breaking of interacting fermion systems constitutes a major challenge for many-body theory due to the proliferation of new independent scattering channels once absent or degenerate in the symmetric phase. One example is given by the ferro/antiferromagnetic broken symmetry phase (BSP) of the Hubbard model, where vertices in the spin-transverse and spin-longitudinal channels become independent with a consequent increase in the computational power for their calculation. We generalise the non-perturbative Two-Particle-Self-Consistent method (TPSC) to address broken SU(2) magnetic phases in the Hubbard model, offering an efficient approach that incorporates strong correlations. We show that in the BSP, the sum-rule enforcement of susceptibilities must be accompanied by a modified gap equation resulting in a renormalisation of the order parameter, vertex corrections and the preservation of the gap-less feature of the Goldstone modes. We then apply the theory to the antiferromagnetic phase of the Hubbard model in the cubic lattice at half-filling. We compare our results of double occupancies and staggered magnetisation to the ones obtained using Diagrammatic Monte Carlo showing excellent quantitative agreement. We demonstrate how vertex corrections play a central role in lowering the Higgs resonance with respect to the quasi-particle excitation gap in the spin-longitudinal susceptibility, yielding a well visible Higgs-mode.

• Codebase release 1.2 for YASTN

  Marek M. Rams, Gabriela Wójtowicz, Aritra Sinha, Juraj Hasik

  SciPost Phys. Codebases 52-r1.2 (2025) · published 26 February 2025 | Toggle abstract · src

  We present an open-source tensor network Python library for quantum many-body simulations. At its core is an Abelian-symmetric tensor, implemented as a sparse block structure managed by a logical layer on top of a dense multidimensional array backend. This serves as the basis for higher-level tensor network algorithms operating on matrix product states and projected entangled pair states. An appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPU and other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD and simulating thermal states of the Hubbard model via imaginary time evolution. For these challenging examples, we identify and quantify sources of the numerical advantage exploited by the symmetric-tensor implementation.

  1 citation

• YASTN: Yet another symmetric tensor networks; A Python library for Abelian symmetric tensor network calculations

  Marek M. Rams, Gabriela Wójtowicz, Aritra Sinha, Juraj Hasik

  SciPost Phys. Codebases 52 (2025) · published 26 February 2025 | Toggle abstract · pdf

  We present an open-source tensor network Python library for quantum many-body simulations. At its core is an Abelian-symmetric tensor, implemented as a sparse block structure managed by a logical layer on top of a dense multidimensional array backend. This serves as the basis for higher-level tensor network algorithms operating on matrix product states and projected entangled pair states. An appropriate backend, such as PyTorch, gives direct access to automatic differentiation (AD) for cost-function gradient calculations and execution on GPU and other supported accelerators. We show the library performance in simulations with infinite projected entangled-pair states, such as finding the ground states with AD and simulating thermal states of the Hubbard model via imaginary time evolution. For these challenging examples, we identify and quantify sources of the numerical advantage exploited by the symmetric-tensor implementation.

  2 citations