Efficient iPEPS Simulation on the Honeycomb Lattice via QR-based CTMRG
- URL: http://arxiv.org/abs/2509.05090v1
- Date: Fri, 05 Sep 2025 13:29:46 GMT
- Title: Efficient iPEPS Simulation on the Honeycomb Lattice via QR-based CTMRG
- Authors: Qi Yang, Philippe Corboz,
- Abstract summary: We develop a QR-based corner transfer matrix renormalization group (CTMRG) framework for contracting infinite projected entangled-pair states (iPEPS) on honeycomb lattices.<n>Our method explicitly uses the lattice's native C3v symmetry at each site, generalizing QR-based acceleration to enable efficient and stable contractions.
- Score: 2.0227357325437034
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We develop a QR-based corner transfer matrix renormalization group (CTMRG) framework for contracting infinite projected entangled-pair states (iPEPS) on honeycomb lattices. Our method explicitly uses the lattice's native C3v symmetry at each site, generalizing QR-based acceleration (previously limited to square lattices) to enable efficient and stable contractions. This approach achieves order-of-magnitude speedups over conventional singular value decomposition (SVD)-based CTMRG while maintaining high numerical precision. Comprehensive benchmark calculations for the spin-1/2 Heisenberg and Kitaev models demonstrate higher computational efficiency without sacrificing accuracy. We further employ our method to study the Kitaev-Heisenberg model, where we provide numerical evidence for the universal 1/r^4 decay of the dimer-dimer correlation function within the quantum spin liquid (QSL) phase. Our work establishes a framework for extending QR-based CTMRG to other lattice geometries, opening new avenues for studying exotic quantum phases with tensor networks.
Related papers
- Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
arXiv Detail & Related papers (2026-01-10T02:36:03Z) - Evaluating Sample-Based Krylov Quantum Diagonalization for Heisenberg Models with Applications to Materials Science [0.0]
We evaluate the Sample-based Krylov Quantum Diagonalization (SKQD) algorithm on one- and two-dimensional Heisenberg models.<n>Using problem-informed initial states and magnetization-sector sweeps, SKQD accurately reproduces ground-state energies and field-dependent magnetization across a range of anisotropies.
arXiv Detail & Related papers (2025-12-19T00:29:06Z) - Sample-Based Krylov Quantum Diagonalization for the Schwinger Model on Trapped-Ion and Superconducting Quantum Processors [26.315169722500556]
We apply the recently proposed Sample-based Krylov Quantum Diagonalization (SKQD) method to lattice gauge theories.<n>We study the dependence of the ground-state energy and particle number on the value of the $theta$-term, accurately capturing the model's phase structure.<n>We show that SKQD substantially reduces the effective Hilbert space, and although the Krylov space dimension still scales exponentially, the slower growth underscores its promise for simulating lattice gauge theories in larger volumes.
arXiv Detail & Related papers (2025-10-30T19:21:06Z) - Molecular Properties in Quantum-Classical Auxiliary-Field Quantum Monte Carlo: Correlated Sampling with Application to Accurate Nuclear Forces [1.2189422792863451]
We extend correlated sampling from classical auxiliary-field quantum Monte Carlo to the quantum-classical (QCAFQMC) framework.<n>We demonstrate significant improvements over single-reference methods in force evaluations for N$ wave$ and stretched linear H$_4$, particularly in strongly correlated regions.<n>We also apply our methodology to the MEA-CO$$ carbon capture reaction, employing quantum information metrics for active space selection and matchgate shadows.
arXiv Detail & Related papers (2025-07-23T23:51:10Z) - Accelerating two-dimensional tensor network contractions using QR-decompositions [3.6498714804297387]
We propose a contraction scheme for $C_4v$-symmetric tensor networks based on combining the corner transfer matrix renormalization group with QR-decompositions.<n>Our approach achieves up to two orders of magnitude speedup compared to standard CTMRG and yields state-of-the-art results.
arXiv Detail & Related papers (2025-05-01T12:48:26Z) - Adaptive Non-local Observable on Quantum Neural Networks [10.617463958884528]
We propose an adaptive non-local measurement framework for quantum circuits.<n>Inspired by the Heisenberg picture, we show that optimizing VQC rotations corresponds to tracing a trajectory in the observable space.<n>We show that properly incorporating variational rotations with non-local observables enhances qubit interaction and information mixture.
arXiv Detail & Related papers (2025-04-18T02:20:12Z) - KPZ scaling from the Krylov space [83.88591755871734]
Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang scaling in late-time correlators and autocorrelators has been reported.
Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis.
arXiv Detail & Related papers (2024-06-04T20:57:59Z) - SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance [136.15885067858298]
This study presents a novel feature-matching-based sparse geometry regularization module, enhanced by a spatially consistent geometry filtering mechanism and a frequency-guided geometric regularization strategy.<n>Our experiments demonstrate that SGCNeRF achieves superior geometry-consistent outcomes and also surpasses FreeNeRF, with improvements of 0.7 dB in PSNR on LLFF and DTU.
arXiv Detail & Related papers (2024-04-01T08:37:57Z) - 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) - A Floquet-Rydberg quantum simulator for confinement in $\mathbb{Z}_2$
gauge theories [44.99833362998488]
Recent advances in the field of quantum technologies have opened up the road for the realization of small-scale quantum simulators.
We present a scalable Floquet scheme for the quantum simulation of the real-time dynamics in a $mathbbZ$ LGT.
We show that an observation of gauge-invariant confinement dynamics in the Floquet-Rydberg setup is at reach of current experimental techniques.
arXiv Detail & Related papers (2023-11-28T13:01:24Z) - MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table [62.164549651134465]
We propose MF-NeRF, a memory-efficient NeRF framework that employs a Mixed-Feature hash table to improve memory efficiency and reduce training time while maintaining reconstruction quality.
Our experiments with state-of-the-art Instant-NGP, TensoRF, and DVGO, indicate our MF-NeRF could achieve the fastest training time on the same GPU hardware with similar or even higher reconstruction quality.
arXiv Detail & Related papers (2023-04-25T05:44:50Z) - D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT)
We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity.
In addition, we show that our approach enables us to explore more complex neural-based wave functions.
arXiv Detail & Related papers (2023-03-01T10:38:10Z) - NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizer [45.47667026025716]
We propose a novel, robust and accelerated iteration that relies on two key elements.
The convergence and stability of the obtained method, referred to as NAG-GS, are first studied extensively.
We show that NAG-arity is competitive with state-the-art methods such as momentum SGD with weight decay and AdamW for the training of machine learning models.
arXiv Detail & Related papers (2022-09-29T16:54:53Z) - Periodic Coupled-Cluster Green's Function for Photoemission Spectra of
Realistic Solids [1.1470070927586016]
We present an efficient implementation of coupled-cluster Green's function (CCGF) method for simulating photoemission spectra of periodic systems.
We propose an active-space self-energy correction scheme by combining CCGF with cheaper many-body perturbation theory (GW) and implement the model order reduction (MOR) frequency perturbation technique.
We find that the active-space self-energy correction and MOR techniques significantly reduce the computational cost of CCGF while maintaining the high accuracy.
arXiv Detail & Related papers (2022-08-16T00:28:05Z) - Optimal Rates for Averaged Stochastic Gradient Descent under Neural
Tangent Kernel Regime [50.510421854168065]
We show that the averaged gradient descent can achieve the minimax optimal convergence rate.
We show that the target function specified by the NTK of a ReLU network can be learned at the optimal convergence rate.
arXiv Detail & Related papers (2020-06-22T14:31:37Z)
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.