Publications

Order by: publication date number of citations

• Codebase release 1.0 for Kolya

  Matteo Fael, Ilija S. Milutin, K. Keri Vos

  SciPost Phys. Codebases 55-r1.0 (2025) · published 25 March 2025 | Toggle abstract · src

  We introduce the code kolya, an open-source tool for phenomenological analyses of inclusive semileptonic $B\to X_c l\bar \nu_l$ decays. It contains a library to compute predictions for the total rate and various kinematic moments within the framework of the heavy quark expansion, utilizing the so-called kinetic scheme. The library currently includes power corrections up to $1/m_b^5$. All available QCD perturbative corrections are implemented via interpolation grids for fast numerical evaluation. We also include effects from new physics parameterized as Wilson coefficients of dimension-six operators in the weak effective theory below the electroweak scale. The library is interfaced to CRunDec for easy evaluation of the quark masses and strong coupling constant at different renormalization scales. The library is developed in Python and does not require compilation. The code is flexible and can be extended to other kinds of inclusive decays such as $B$ meson lifetimes, $B \to X_u l\bar \nu_l$ and $D$ meson decays.

  1 citation

• Kolya: An open-source package for inclusive semileptonic B decays

  Matteo Fael, Ilija S. Milutin, K. Keri Vos

  SciPost Phys. Codebases 55 (2025) · published 25 March 2025 | Toggle abstract · pdf

  We introduce the code kolya, an open-source tool for phenomenological analyses of inclusive semileptonic $B\to X_c l\bar \nu_l$ decays. It contains a library to compute predictions for the total rate and various kinematic moments within the framework of the heavy quark expansion, utilizing the so-called kinetic scheme. The library currently includes power corrections up to $1/m_b^5$. All available QCD perturbative corrections are implemented via interpolation grids for fast numerical evaluation. We also include effects from new physics parameterized as Wilson coefficients of dimension-six operators in the weak effective theory below the electroweak scale. The library is interfaced to CRunDec for easy evaluation of the quark masses and strong coupling constant at different renormalization scales. The library is developed in Python and does not require compilation. The code is flexible and can be extended to other kinds of inclusive decays such as $B$ meson lifetimes, $B \to X_u l\bar \nu_l$ and $D$ meson decays.

  1 citation

• 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

• Quantum many-body simulations with PauliStrings.jl

  Nicolas Loizeau, J. Clayton Peacock, Dries Sels

  SciPost Phys. Codebases 54 (2025) · published 24 March 2025 | Toggle abstract · pdf

  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

• 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

• 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

• 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

• SOLAX: A Python solver for fermionic quantum systems with neural network support

  Louis Thirion, Philipp Hansmann, Pavlo Bilous

  SciPost Phys. Codebases 51 (2025) · published 20 February 2025 | Toggle abstract · pdf

  Numerical modeling of fermionic many-body quantum systems presents similar challenges across various research domains, necessitating universal tools, including state-of-the-art machine learning techniques. Here, we introduce SOLAX, a Python library designed to compute and analyze fermionic quantum systems using the formalism of second quantization. SOLAX provides a modular framework for constructing and manipulating basis sets, quantum states, and operators, facilitating the simulation of electronic structures and determining many-body quantum states in finite-size Hilbert spaces. The library integrates machine learning capabilities to mitigate the exponential growth of Hilbert space dimensions in large quantum clusters. The core low-level functionalities are implemented using the recently developed Python library JAX. Demonstrated through its application to the Single Impurity Anderson Model, SOLAX offers a flexible and powerful tool for researchers addressing the challenges of many-body quantum systems across a broad spectrum of fields, including atomic physics, quantum chemistry, and condensed matter physics.

  2 citations

• Codebase release 1.0 for SOLAX

  Louis Thirion, Philipp Hansmann, Pavlo Bilous

  SciPost Phys. Codebases 51-r1.0 (2025) · published 20 February 2025 | Toggle abstract · src

  Numerical modeling of fermionic many-body quantum systems presents similar challenges across various research domains, necessitating universal tools, including state-of-the-art machine learning techniques. Here, we introduce SOLAX, a Python library designed to compute and analyze fermionic quantum systems using the formalism of second quantization. SOLAX provides a modular framework for constructing and manipulating basis sets, quantum states, and operators, facilitating the simulation of electronic structures and determining many-body quantum states in finite-size Hilbert spaces. The library integrates machine learning capabilities to mitigate the exponential growth of Hilbert space dimensions in large quantum clusters. The core low-level functionalities are implemented using the recently developed Python library JAX. Demonstrated through its application to the Single Impurity Anderson Model, SOLAX offers a flexible and powerful tool for researchers addressing the challenges of many-body quantum systems across a broad spectrum of fields, including atomic physics, quantum chemistry, and condensed matter physics.

  1 citation