Chu Guo

The Grassmann time-evolving matrix product operator (GTEMPO) method, which represents the Feynman-Vernon influence functional as a temporal matrix product state, has been shown to be a flexible and potentially scalable solution for fermionic quantum impurity problems. In this work, we extend GTEMPO to solve fermionic impurity problems in the Nambu formalism, in which the impurity is coupled to a superconducting bath. A key insight is that by employing the Bogoliubov transformation for the superconducting bath, one could obtain the analytic expression of the Feynman-Vernon influence functional in a similar form to the case of a normal bath, after which the core algorithms of GTEMPO can be straightforwardly adapted. We demonstrate the accuracy of our method by benchmarking it against exact diagonalization in several exactly solvable cases, and against the continuous-time quantum Monte Carlo method using converged dynamical mean-field theory iterations on the imaginary contour in the nonintegrable case. In all cases, we perform both imaginary- and real-time calculations to illustrate the flexibility of our method. These results illustrate that our method, termed Nambu-GTEMPO, could be potentially useful as a quantum impurity solver to study the superconducting states in both equilibrium and nonequilibrium systems.

The polaron problem is a very old problem in condensed matter physics that dates back to the thirties, but still remains largely unsolved today, especially when electron-electron interaction is taken into consideration. The presence of both electron-electron and electron-phonon interactions in the problem invalidates most existing numerical methods, either computationally too expensive or simply intractable. The continuous time quantum Monte Carlo (CTQMC) methods could tackle this problem, but are only effective on the imaginary-time axis. In this work we present a method based on tensor network and the path integral formalism to solve polaron impurity problems. As both the electron and phonon baths can be integrated out via the Feynman-Vernon influence functional in the path integral formalism, our method is free of bath discretization error. It can also flexibly work on the imaginary, Keldysh, and the L-shaped Kadanoff contour. In addition, our method can naturally resolve several long-existing challenges: (i) non-diagonal hybridization function; (ii) measuring multi-time correlations beyond the single particle Green’s functions. We demonstrate the effectiveness and accuracy of our method with extensive numerical examples against analytic solutions, exact diagonalization and CTQMC. We also perform full-fledged real-time calculations that have never been done before to our knowledge, which could be a benchmarking baseline for future method developments.

Accurately solving the Schrödinger equation for intricate systems remains a prominent challenge in physical sciences. Here we present QiankunNet, a neural network quantum state (NNQS) framework that combines Transformer architectures with efficient autoregressive sampling to solve the many-electron Schrödinger equation. At its core is a Transformer-based wave function ansatz that captures complex quantum correlations through attention mechanisms, effectively learning the structure of many-body states. The quantum state sampling employs layer-wise Monte Carlo tree search (MCTS) that naturally enforces electron number conservation while exploring orbital configurations. The framework incorporates physics-informed initialization using truncated configuration interaction solutions, providing principled starting points for variational optimization. Our systematic benchmarks demonstrate QiankunNet’s versatility across different chemical systems. For molecular systems up to 30 spin orbitals, we achieved correlation energies reaching 99.9% of the full configuration interaction (FCI) benchmark, setting a new standard for neural network quantum states. Most notably, in treating the Fenton reaction mechanism, a fundamental process in biological oxidative stress, QiankunNet successfully handled a large CAS(46e,26o) active space, enabling accurate description of the complex electronic structure evolution during Fe(II) to Fe(III) oxidation.