We present the first open release of the DCA++ project, a high-performance research software framework to solve quantum many-body problems with cutting edge quantum cluster algorithms. DCA++ implements the dynamical cluster approximation (DCA) and its DCA+ extension with a continuous self-energy. The algorithms capture nonlocal correlations in strongly correlated electron systems, thereby giving insight into high-Tc superconductivity. The code’s scalability allows efficient usage of systems at all scales, from workstations to leadership computers. With regard to the increasing heterogeneity of modern computing machines, DCA++ provides portable performance on conventional and emerging new architectures, such as hybrid CPU–GPU, sustaining multiple petaflops on ORNL’s Titan and CSCS’ Piz Daint supercomputers. Moreover, we show how sustainable and scalable development of the code base has been achieved by adopting standard techniques of the software industry. These include employing a distributed version control system, applying test-driven development and following continuous integration. Program Title: DCA++ Program Files doi: http://dx.doi.org/10.17632/482jm5cv77.1 Licensing provisions: BSD-3-Clause Programming language: C++14 and CUDA Nature of problem: Understanding the fascinating physics of strongly correlated electron systems requires the development of sophisticated algorithms and their implementation on leadership computing systems. Solution method: The DCA++ code provides a highly scalable and efficient implementation of the dynamical cluster approximation (DCA) and its DCA+ extension.
We present the first open release of the DCA++ project, a high-performance research software framework to solve quantum many-body problems with cutting edge quantum cluster algorithms. DCA++ implements the dynamical cluster approximation (DCA) and its DCA+ extension with a continuous self-energy. The algorithms capture nonlocal correlations in strongly correlated electron systems, thereby giving insight into high-Tc superconductivity. The code’s scalability allows efficient usage of systems at all scales, from workstations to leadership computers. With regard to the increasing heterogeneity of modern computing machines, DCA++ provides portable performance on conventional and emerging new architectures, such as hybrid CPU–GPU, sustaining multiple petaflops on ORNL’s Titan and CSCS’ Piz Daint supercomputers. Moreover, we show how sustainable and scalable development of the code base has been achieved by adopting standard techniques of the software industry. These include employing a distributed version control system, applying test-driven development and following continuous integration. Program Title: DCA++ Program Files doi: http://dx.doi.org/10.17632/482jm5cv77.1 Licensing provisions: BSD-3-Clause Programming language: C++14 and CUDA Nature of problem: Understanding the fascinating physics of strongly correlated electron systems requires the development of sophisticated algorithms and their implementation on leadership computing systems. Solution method: The DCA++ code provides a highly scalable and efficient implementation of the dynamical cluster approximation (DCA) and its DCA+ extension.
The dynamical cluster approximation (DCA) is a systematic extension beyond the single-site approximation in dynamical mean field theory, to include spatially nonlocal correlations in quantum many-body simulations of strongly correlated systems. We extend the DCA with a continuous lattice self-energy in order to achieve better convergence with cluster size. This method, which we call DCA+, cures the cluster-shape dependence problems of the DCA, without suffering from causality violations of previous attempts to interpolate the cluster self-energy. A practical approach based on standard inference techniques is given to deduce the continuous lattice self-energy from an interpolated cluster self-energy. We study the pseudogap region of a hole-doped two-dimensional Hubbard model and find that, in the DCA+algorithm, the self-energy and pseudogap temperature T∗ converge monotonously with cluster size. Introduction of a continuous lattice self-energy eliminates artificial long-range correlations and thus significantly reduces the sign problem of the quantum Monte Carlo cluster solver in the DCA+algorithm compared to the normal DCA. Simulations with much larger cluster sizes thus become feasible, which, along with the improved convergence in cluster size, raises hope that precise extrapolations to the exact infinite cluster size limit can be reached for other physical quantities as well.
Recent developments in the ChronusQuantum (ChronusQ) software package highlight its advanced capabilities in simulating quantum dynamics of both electrons and nuclei, accurately accounting for relativistic and correlation effects, and leveraging high-performance implementations tailored for modern heterogeneous, massively parallel computing architectures. ChronusQ is an open-source tool released under the GNU General Public License v2, designed to solve complex problems in ab initio electronic structure theory. It is developed by a collaborative team across leading research universities and is renowned for its integrated time-dependent quantum mechanical theories, spectral analysis tools, and variational relativistic frameworks. With support from the Office of Advanced Cyberinfrastructure and the Division of Chemistry at the National Science Foundation, the team has expanded ChronusQ's capabilities in several significant directions. We have developed the nuclear-electronic orbital time- dependent configuration interaction (NEO-TDCI) method, which enables accurate treatment of hydrogen tunneling and double excitations—cases where multiconfigurational approaches are essential. Additionally, we introduced the polarizable continuum model and gauge-invariant atomic orbitals, both compatible with NEO, to simulate solvent effects and magnetic properties. ChronusQ has also significantly advanced its relativistic simulation toolkit with the implementation of a four-component relativistic framework, offering a robust foundation for simulating the quantum behavior of heavy atoms, where relativistic effects are particularly pronounced. Building on this, ChronusQ now integrates relativistic coupled cluster (CC) framework and the distributed active space (DAS) framework, providing highly accurate treatments of electron correlation. Key modules in the NEO, CC, and DAS frameworks are implemented with MPI and GPU-parallelized computation. Notably, our DAS framework has set a new benchmark by surpassing the quadrillion-determinant barrier in a numerically exact configuration interaction method. This year, we also enhanced the user and developer experience by redesigning ChronusQ's module structure to support flexible extension and integration of existing features, reliable restart of interrupted calculations, and seamless interfacing with other computational chemistry software. The physical insights enabled by ChronusQ continue to drive innovations in sustainable energy, catalysis, quantum computing, and other domains with immediate societal impact.
We present release 3.0 of the ALPS (Algorithms and Libraries for Physics Simulations) project, an open-source software project to develop libraries and application programs for the simulation of strongly correlated quantum lattice models such as quantum magnets, lattice bosons, and strongly correlated fermion systems. As in previous releases, development is centered on common data formats, on libraries to simplify and speed up code development, and on full-featured simulation programs that let non-experts carry out serial or parallel numerical simulations using the important algorithms for quantum lattice models: classical and quantum Monte Carlo (QMC) using non-local updates, extended-ensemble simulations, exact and full diagonalization (ED), the density matrix renormalization group (DMRG), and continuous-time QMC solvers for dynamical mean-field theory (DMFT). Major changes in release 3.0 include distribution of the pyalps binary through the Python Package Index (pip install pyalps) and through Spack for HPC systems; migration of development to GitHub with continuous integration and automated testing; relicensing of the package under the permissive MIT license; a rebuilt documentation and tutorial website, including a set of Jupyter-notebook tutorials and localized content; a broad modernization of the C++ codebase (C++17 compliance, Boost and NumPy 2.0 compatibility, and warning and dead-code cleanup) together with a major DMRG update and associated reliability and build-compatibility fixes; the removal of legacy components (the VisTrails provenance integration and the TEBD, MPS, and directed-worm-algorithm application codes); archival of release 3.0.0 with a Zenodo DOI; and a formal governance and sustainability model developed under the US National Science Foundation (NSF) POSE program. The software is available at https://alps.comp-phys.org.
The dynamical cluster approximation (DCA) is a quantum cluster extension to the single-site dynamical mean-field theory that incorporates spatially nonlocal dynamic correlations systematically and nonperturbatively. The DCA+ algorithm addresses the cluster shape dependence of the DCA and improves the convergence with cluster size by introducing a lattice self-energy with continuous momentum dependence. However, we show that the DCA+ algorithm is plagued by a fundamental problem when its self-consistency equations are formulated using the bare Green's function of the cluster. This problem is most severe in the strongly correlated regime at low doping, where the DCA+ self-energy becomes overly metallic and local, and persists to cluster sizes where the standard DCA has long converged. In view of the failure of the DCA+ algorithm, we propose to complement DCA simulations with a post-interpolation procedure for single-particle and two-particle correlation functions to preserve continuous momentum dependence and the associated benefits in the DCA. We demonstrate the effectiveness of this practical approach with results for the half-filled and hole-doped two-dimensional Hubbard model.
We present a new quantum cluster algorithm to simulate models of high-Tc superconductors. This algorithm extends current methods with continuous lattice self-energies, thereby removing artificial long-range correlations. This cures the fermionic sign problem in the underlying quantum Monte Carlo solver for large clusters and realistic values of the Coulomb interaction in the entire temperature range of interest. We find that the new algorithm improves time-to-solution by nine orders of magnitude compared to current, state of the art quantum cluster simulations. An efficient implementation is given, which ports to multi-core as well as hybrid CPU-GPU systems. Running on 18,600 nodes on ORNL's Titan supercomputer enables us to compute a converged value of Tc/t = 0.053 ± 0.0014 for a 28 site cluster in the 2D Hubbard model with U/t = 7 at 10% hole doping. Typical simulations on Titan sustain between 9.2 and 15.4 petaflops (double precision measured over full run), depending on configuration and parameters used.
The DCA+algorithm was recently introduced by Stear, Maier, and Schulthess [Phys. Rev. B 88, 115101 (2013)] to extend the dynamic cluster approximation (DCA) with a continuous lattice self-energy in order to achieve better convergence with cluster size. Here we extend the DCA+algorithm to the calculation of two-particle correlation functions by introducing irreducible vertex functions with continuous momentum dependence consistent with the DCA+self-energy. This enables a significantly more controlled and reliable study of phase transitions than with the DCA. We test the new method by calculating the superconducting transition temperature Tc in the attractive Hubbard model and show that it reproduces previous determinantal quantum Monte Carlo results. We then calculate Tc in the doped repulsive Hubbard model, for which previous DCA calculations could only access the weak-coupling (U=4t) regime for large clusters. We show that the new algorithm provides access to much larger clusters and delivers asymptotically converged results for Tc for both the weak (U=4t) and intermediate (U=7t) coupling regimes, and thereby enables the accurate determination of the exact infinite cluster size result.