A Critical Assessment of the Sample-Based Quantum Diagonalization for Heisenberg and Hubbard Models
- URL: http://arxiv.org/abs/2605.02494v2
- Date: Wed, 06 May 2026 13:11:13 GMT
- Title: A Critical Assessment of the Sample-Based Quantum Diagonalization for Heisenberg and Hubbard Models
- Abstract summary: We show that sample-based quantum diagonalization (SQD) constructs subspaces from computational-basis configurations obtained via measurements of a quantum state.<n>We investigate this assumption by analyzing SQD subspaces constructed directly from configurations of exact ground states of Heisenberg and Hubbard model lattices.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Sample-based quantum diagonalization (SQD) constructs subspaces from computational-basis configurations obtained via measurements of a quantum state, with the goal of approximating low-energy eigenspaces of many-body Hamiltonians. The effectiveness of this approach relies on the assumption that physically relevant states admit a compact representation in the computational basis. We investigate this assumption by analyzing SQD subspaces constructed directly from configurations of exact ground states of Heisenberg and Hubbard model lattices. By eliminating state-preparation and measurement inefficiencies, we isolate the intrinsic configuration-space structure of the wavefunction. We determine the minimal number of configurations required to reproduce the ground-state energy within fixed accuracy thresholds and find that this number grows exponentially with the system size. Notably, this scaling persists even under optimal inclusion of configurations in order of decreasing probability, demonstrating that it originates from intrinsic delocalization of the wavefunction rather than sampling inefficiencies. Our results indicate that SQD effectively probes the configuration-space entropy but faces fundamental scalability limitations for these models.
Related papers
- Improved quantum sampling methods for molecular simulations [0.0]
We show that sample-based quantum diagonalization (SQD) performance can be strongly influenced by uncontrolled growth of the classical diagonalization subspace.<n>We then address the problem of efficiently discovering physically relevant, energy-lowering configurations by introducing a measurement protocol based on non-orthogonal configuration interaction (NOCI)<n>These improvements persist even under fixed classical resource budgets, demonstrating that the resulting configurations are of higher quality rather than being more numerous.
arXiv Detail & Related papers (2026-08-12T02:27:06Z) - Fault tolerant computation of the static structure factor and finite size effects [43.936586633107396]
We develop a quantum post-processing strategy for the leading two-body finite-size correction.<n>After one-body shell effects are reduced by twist averaging, the dominant residual error is controlled by long-wavelength density fluctuations.<n>We also introduce adaptive global and local binary search procedures for identifying the infrared fitting window used to reconstruct the two-body finite size error correction.
arXiv Detail & Related papers (2026-06-26T05:31:08Z) - Filter-assisted quantum subspace diagonalization via wavefunction sparsity engineering [3.551170135656655]
Subspace diagonalization techniques based on quantum sampling have emerged as promising approaches for approximating ground-state energies of many-body systems.<n>Here, we introduce a filter-assisted quantum diagonalization protocol that engineers wavefunction sparsity via a quantum filter.<n>Our results show that the proposed protocol significantly enhances wavefunction sparsity, reduces ground-state energy estimation errors by orders of magnitude, and substantially lowers sampling overhead.
arXiv Detail & Related papers (2026-05-27T06:42:58Z) - 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) - Qumode-Based Variational Quantum Eigensolver for Molecular Excited States [43.148034499498586]
We introduce the Qumode Subspace Variational Quantum Eigensolver (QSS-VQE), a hybrid quantum-classical algorithm for computing molecular excited states.<n>We demonstrate the performance of QSS-VQE through simulations of molecular excited states, including dihydrogen and a conical intersection in cytosine.
arXiv Detail & Related papers (2025-09-05T00:53:51Z) - Grassmann Variational Monte Carlo with neural wave functions [45.935798913942904]
We formalize the framework introduced by Pfau et al.citepfau2024accurate in terms of Grassmann geometry of the Hilbert space.<n>We validate our approach on the Heisenberg quantum spin model on the square lattice, achieving highly accurate energies and physical observables for a large number of excited states.
arXiv Detail & Related papers (2025-07-14T13:53:13Z) - Message-Passing Neural Quantum States for the Homogeneous Electron Gas [41.94295877935867]
We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space.
We demonstrate its accuracy by simulating the ground state of the homogeneous electron gas in three spatial dimensions.
arXiv Detail & Related papers (2023-05-12T04:12:04Z) - Reduced basis surrogates for quantum spin systems based on tensor
networks [0.0]
We show how a greedy strategy to assemble the reduced basis can be implemented.
observables required for the computation of phase diagrams can be computed independent of the underlying Hilbert space.
We illustrate the efficiency and accuracy of this approach for different one-dimensional quantum spin-1 models, including anisotropic as well as biquadratic exchange interactions.
arXiv Detail & Related papers (2023-04-26T14:29:58Z) - Numerical Simulations of Noisy Quantum Circuits for Computational
Chemistry [51.827942608832025]
Near-term quantum computers can calculate the ground-state properties of small molecules.
We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
arXiv Detail & Related papers (2021-12-31T16:33:10Z) - Bosonic field digitization for quantum computers [62.997667081978825]
We address the representation of lattice bosonic fields in a discretized field amplitude basis.
We develop methods to predict error scaling and present efficient qubit implementation strategies.
arXiv Detail & Related papers (2021-08-24T15:30:04Z) - State preparation and measurement in a quantum simulation of the O(3)
sigma model [65.01359242860215]
We show that fixed points of the non-linear O(3) sigma model can be reproduced near a quantum phase transition of a spin model with just two qubits per lattice site.
We apply Trotter methods to obtain results for the complexity of adiabatic ground state preparation in both the weak-coupling and quantum-critical regimes.
We present and analyze a quantum algorithm based on non-unitary randomized simulation methods.
arXiv Detail & Related papers (2020-06-28T23:44:12Z)
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.