Quantum computing for effective nuclear lattice model

• URL: http://arxiv.org/abs/2604.13430v1
• Date: Wed, 15 Apr 2026 03:02:44 GMT
• Title: Quantum computing for effective nuclear lattice model
• Abstract summary: Nuclear lattice effective field theory has become an important framework for quantum many-body calculations in nuclear physics.<n>We develop a quantum-computing framework for a three-dimensional nuclear lattice model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nuclear lattice effective field theory has become an important framework for quantum many-body calculations in nuclear physics, yet its classical implementation remains increasingly challenging for more general interactions and larger systems. In this work, we develop a quantum-computing framework for a three-dimensional nuclear lattice model. We construct a variational quantum eigensolver framework and systematically compare the Jordan-Wigner and Gray code encodings. Our analysis shows that for the few-body systems considered here, Gray code combined with symmetry reduction yields a substantially more compact qubit representation. Based on this framework, we perform numerical studies for $^{2}\mathrm{H}$, $^{3}\mathrm{H}$, and $^{4}\mathrm{He}$ on finite lattices. The calculated ground-state energies exhibit a clear approach toward the corresponding experimental binding energies as the lattice size increases. These results provide a proof-of-principle foundation for future quantum simulations of nuclear many-body problems.

Related papers

• Nuclear Many-Body Systems as Benchmarks for Quantum Computing [39.146761527401424]
  We present a framework for benchmarking quantum algorithms for nuclear many-body systems.<n>We map nuclear interactions in second quantization formalism to qubit Hamiltonians.<n>We provide resource estimates for three representative eigenvalue algorithms.
  arXiv  Detail & Related papers  (2026-07-09T01:53:39Z)
• The free energy limit of the SYK model at high temperature [76.01164590495135]
  The Sachdev-Ye-Kitaev (SYK) model is a disordered quantum mean-field studied condensed matter physics and the theory of black holes.<n>We rigorously compute the free energy limit (annealed and quenched) for this model at high enough but constant temperature.
  arXiv  Detail & Related papers  (2026-05-04T16:07:00Z)
• Efficacious qubit mappings for quantum simulations of the $^{12}$C rotational band [0.0]
  We present first quantum simulations based on the variational quantum eigensolver for the low-lying structure of the $12$C nucleus.<n>We utilize an almost perfect symmetry of atomic nuclei that, in a complete symmetry-adapted basis, drastically reduces the size of the model space.
  arXiv  Detail & Related papers  (2024-12-09T20:39:22Z)
• 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)
• Nuclear shell-model simulation in digital quantum computers [1.5478949073334747]
  We present a shell-model quantum circuit design strategy to find nuclear ground states. We quantify the circuit depth, width and number of gates to encode realistic shell-model wavefunctions.
  arXiv  Detail & Related papers  (2023-02-07T17:48:19Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Toward nuclear physics from lattice QCD on quantum computers [0.0]
  We discuss the computational complexities of classical and quantum simulations of lattice QCD. It is shown that the quantum simulation scales better as a function of a nucleon number and thus will outperform for large nuclei.
  arXiv  Detail & Related papers  (2022-11-26T12:09:40Z)
• Spectral density reconstruction with Chebyshev polynomials [77.34726150561087]
  We show how to perform controllable reconstructions of a finite energy resolution with rigorous error estimates. This paves the way for future applications in nuclear and condensed matter physics.
  arXiv  Detail & Related papers  (2021-10-05T15:16:13Z)
• Photon-mediated Stroboscopic Quantum Simulation of a $\mathbb{Z}_{2}$ Lattice Gauge Theory [58.720142291102135]
  Quantum simulation of lattice gauge theories (LGTs) aims at tackling non-perturbative particle and condensed matter physics. One of the current challenges is to go beyond 1+1 dimensions, where four-body (plaquette) interactions, not contained naturally in quantum simulating devices, appear. We show how to prepare the ground state and measure Wilson loops using state-of-the-art techniques in atomic physics.
  arXiv  Detail & Related papers  (2021-07-27T18:10:08Z)
• Engineering analog quantum chemistry Hamiltonians using cold atoms in optical lattices [69.50862982117127]
  We benchmark the working conditions of the numerically analog simulator and find less demanding experimental setups. We also provide a deeper understanding of the errors of the simulation appearing due to discretization and finite size effects.
  arXiv  Detail & Related papers  (2020-11-28T11:23:06Z)
• Variational Monte Carlo calculations of $\mathbf{A\leq 4}$ nuclei with an artificial neural-network correlator ansatz [62.997667081978825]
  We introduce a neural-network quantum state ansatz to model the ground-state wave function of light nuclei. We compute the binding energies and point-nucleon densities of $Aleq 4$ nuclei as emerging from a leading-order pionless effective field theory Hamiltonian.
  arXiv  Detail & Related papers  (2020-07-28T14:52:28Z)

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.