Clifford augmented density matrix renormalization group for \textit{ab initio} quantum chemistry

• URL: http://arxiv.org/abs/2506.16026v1
• Date: Thu, 19 Jun 2025 04:48:52 GMT
• Title: Clifford augmented density matrix renormalization group for \textit{ab initio} quantum chemistry
• Authors: Lizhong Fu, Honghui Shang, Jinlong Yang, Chu Guo,
• Abstract summary: We propose an efficient scheme in CA-DMRG to deal with textitab initio quantum chemistry Hamiltonians.<n>Our numerical results show that CA-DMRG can reach several orders of magnitude higher accuracy than DMRG using the same bond dimension.
• Score: 3.632603573330658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recently proposed Clifford augmented density matrix renormalization group (CA-DMRG) method seamlessly integrates Clifford circuits with matrix product states, and takes advantage of the expression power from both. CA-DMRG has been shown to be able to achieve higher accuracy than standard DMRG on commonly used lattice models, with only moderate computational overhead compared to the latter. In this work, we propose an efficient scheme in CA-DMRG to deal with \textit{ab initio} quantum chemistry Hamiltonians, and apply it to study several molecular systems. Our numerical results show that CA-DMRG can reach several orders of magnitude higher accuracy than DMRG using the same bond dimension, pointing out a promising route to push the boundary of solving \textit{ab initio} quantum chemistry with strong static correlations.

Related papers

• ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions [64.17845687013434]
  We propose ReDiSC, a structured diffusion model for structured node classification.<n>We show that ReDiSC achieves superior or highly competitive performance compared to state-of-the-art GNN, label propagation, and diffusion-based baselines.<n> Notably, ReDiSC scales effectively to large-scale datasets on which previous structured diffusion methods fail due to computational constraints.
  arXiv  Detail & Related papers  (2025-07-19T04:46:53Z)
• Multi-Target Density Matrix Renormalization Group X algorithm and its application to circuit quantum electrodynamics [0.023787965910387825]
  We employ a variant of the density matrix renormalization group (DMRG) algorithm, DMRG-X, to efficiently obtain localized eigenstates of a 2D transmon array.<n>We also introduce MTDMRG-X, a new algorithm that combines DMRG-X with multi-target DMRG to efficiently compute excited states even in regimes with strong eigenstate hybridization.
  arXiv  Detail & Related papers  (2025-06-30T17:55:20Z)
• Wavefunction optimization at the complete basis set limit with Multiwavelets and DMRG [0.0]
  We develop an algorithm integrating DMRG within the multiwavelet-based multiresolution analysis (MRA)<n>We adopt a pre-existing Lagrangian optimization algorithm for orbitals represented in the MRA domain and improve its computational efficiency.<n>We apply our method to small systems such as H2, He, HeH2, BeH2 and N2.
  arXiv  Detail & Related papers  (2025-03-13T18:59:03Z)
• Clifford-Dressed Variational Principles for Precise Loschmidt Echoes [44.99833362998488]
  We extend the recently introduced Clifford dressed Time-Dependent Variational Principle (TDVP) to efficiently compute many-body wavefunction amplitudes in the computational basis.<n>By incorporating Clifford disentangling gates during TDVP evolution, our method effectively controls entanglement growth while keeping the computation of these amplitudes accessible.
  arXiv  Detail & Related papers  (2025-02-03T22:43:32Z)
• Pushing the Limits of Large Language Model Quantization via the Linearity Theorem [71.3332971315821]
  We present a "line theoremarity" establishing a direct relationship between the layer-wise $ell$ reconstruction error and the model perplexity increase due to quantization. This insight enables two novel applications: (1) a simple data-free LLM quantization method using Hadamard rotations and MSE-optimal grids, dubbed HIGGS, and (2) an optimal solution to the problem of finding non-uniform per-layer quantization levels.
  arXiv  Detail & Related papers  (2024-11-26T15:35:44Z)
• A Fresh Look at Generalized Category Discovery through Non-negative Matrix Factorization [83.12938977698988]
  Generalized Category Discovery (GCD) aims to classify both base and novel images using labeled base data. Current approaches inadequately address the intrinsic optimization of the co-occurrence matrix $barA$ based on cosine similarity. We propose a Non-Negative Generalized Category Discovery (NN-GCD) framework to address these deficiencies.
  arXiv  Detail & Related papers  (2024-10-29T07:24:11Z)
• A perturbative approach to the solution of the Thirring quantum cellular automaton [42.205102271729665]
  The Thirring Quantum Cellular Automaton (QCA) describes the discrete time dynamics of local fermionic modes that evolve according to one step of the Dirac cellular automaton followed by the most general on-site number-preserving interaction, and serves as the QCA counterpart of the Thirring model in quantum field theory.
  arXiv  Detail & Related papers  (2024-06-28T13:44:10Z)
• Augmenting Density Matrix Renormalization Group with Clifford Circuits [0.49157446832511503]
  We present the seamless integration of Clifford circuits within the Density Matrix Renormalization Group algorithm. This framework is useful not only for its current application but also for its potential to be easily adapted to various other numerical approaches.
  arXiv  Detail & Related papers  (2024-05-15T09:53:08Z)
• Quantum State Transfer in Interacting, Multiple-Excitation Systems [41.94295877935867]
  Quantum state transfer (QST) describes the coherent passage of quantum information from one node to another. We describe Monte Carlo techniques which enable the discovery of a Hamiltonian that gives high-fidelity QST. The resulting Jaynes-Cummings-Hubbard and periodic Anderson models can, in principle, be engineered in appropriate hardware to give efficient QST.
  arXiv  Detail & Related papers  (2024-05-10T23:46:35Z)
• Quantization of Large Language Models with an Overdetermined Basis [73.79368761182998]
  We introduce an algorithm for data quantization based on the principles of Kashin representation. Our findings demonstrate that Kashin Quantization achieves competitive or superior quality in model performance.
  arXiv  Detail & Related papers  (2024-04-15T12:38:46Z)
• Neutron-nucleus dynamics simulations for quantum computers [49.369935809497214]
  We develop a novel quantum algorithm for neutron-nucleus simulations with general potentials. It provides acceptable bound-state energies even in the presence of noise, through the noise-resilient training method. We introduce a new commutativity scheme called distance-grouped commutativity (DGC) and compare its performance with the well-known qubit-commutativity scheme.
  arXiv  Detail & Related papers  (2024-02-22T16:33:48Z)
• Geometric Clifford Algebra Networks [53.456211342585824]
  We propose Geometric Clifford Algebra Networks (GCANs) for modeling dynamical systems. GCANs are based on symmetry group transformations using geometric (Clifford) algebras.
  arXiv  Detail & Related papers  (2023-02-13T18:48:33Z)
• On the Ordering of Sites in the Density Matrix Renormalization Group using Quantum Mutual Information [0.0]
  The density matrix renormalization group (DMRG) of White 1992 remains to this day an integral component of many state-of-the-art methods for efficiently strongly correlated quantum systems. In quantum chemistry, QC-DMRG became a powerful tool for ab initio calculations with the non-relativistic Schr"odinger equation.
  arXiv  Detail & Related papers  (2021-03-01T16:24:37Z)
• Transcorrelated Density Matrix Renormalization Group [0.0]
  We introduce the transcorrelated Density Matrix Renormalization Group (tcDMRG) theory for the efficient approximation of the energy for strongly correlated systems. tcDMRG encodes the wave function as a product of a fixed Jastrow or Gutzwiller correlator and a matrix product state. We demonstrate fast energy convergence of tcDMRG, which indicates that tcDMRG could increase the efficiency of standard DMRG beyond quasi-monodimensional systems.
  arXiv  Detail & Related papers  (2020-09-05T23:20:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.