A Quantum Annealing Protocol to Solve the Nuclear Shell Model

• URL: http://arxiv.org/abs/2411.06954v2
• Date: Tue, 17 Dec 2024 17:48:18 GMT
• Title: A Quantum Annealing Protocol to Solve the Nuclear Shell Model
• Authors: Emanuele Costa, Axel Perez-Obiol, Javier Menendez, Arnau Rios, Artur Garcia-Saez, Bruno Julia-Diaz,
• Abstract summary: We propose a tailored driver Hamiltonian that preserves a large gap and validate our approach in a dozen setups with basis sizes up to $105$.<n>While the nuclear Hamiltonian is non-local and thus challenging to implement in current setups, the estimated computational cost is challenging in the many-body basis size.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The nuclear shell model accurately describes the structure and dynamics of atomic nuclei. However, the exponential scaling of the basis size with the number of degrees of freedom hampers a direct numerical solution for heavy nuclei. In this work, we present a quantum annealing protocol to obtain nuclear ground states. We propose a tailored driver Hamiltonian that preserves a large gap and validate our approach in a dozen nuclei with basis sizes up to $10^5$ using classical simulations of the annealing evolution. We explore the relation between the spectral gap and the total time of the annealing protocol, assessing its accuracy by comparing the fidelity and energy relative error to classical benchmarks. While the nuclear Hamiltonian is non-local and thus challenging to implement in current setups, the estimated computational cost of our annealing protocol on quantum circuits is polynomial in the many-body basis size, paving the way to study heavier nuclei.

Related papers

• Nuclear responses with neural-network quantum states [37.902436796793616]
  We introduce a variational Monte Carlo framework that combines neural-network quantum states with the Lorentz integral transform technique. We focus on the photoabsorption cross section of light nuclei, where benchmarks against numerically exact techniques are available.
  arXiv  Detail & Related papers  (2025-04-28T18:57:21Z)
• Global Framework for Emulation of Nuclear Calculations [0.0]
  We develop a hierarchical framework that combines ab initio many-body calculations with a Bayesian neural network. We benchmark our developments using the oxygen isotopic chain, achieving accurate results for ground-state energies and nuclear charge radii. Our framework enables global sensitivity analysis of nuclear binding energies and charge radii with respect to the low-energy constants that describe the nuclear force.
  arXiv  Detail & Related papers  (2025-02-27T18:34:56Z)
• 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)
• Quantum entanglement patterns in the structure of atomic nuclei within the nuclear shell model [1.4608076589541017]
  We analyze the structure of light and medium-mass berillyum, oxygen, neon and calcium isotopes within the nuclear shell model. We use different entanglement metrics, including single-orbital entanglement, mutual information, and von Neumann entropies. This analysis provides a guide for designing more efficient quantum algorithms for the noisy intermediate-scale quantum era.
  arXiv  Detail & Related papers  (2023-07-11T12:08:25Z)
• 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)
• Deep-neural-network approach to solving the ab initio nuclear structure problem [0.799536002595393]
  We develop FeynmanNet, a deep-learning variational quantum Monte Carlo approach for emphab initio nuclear structure. We show that FeynmanNet can provide very accurate solutions of ground-state energies and wave functions for $4$He, $6$Li, and even up to $16$O.
  arXiv  Detail & Related papers  (2022-11-25T10:14:04Z)
• Solving the nuclear pairing model with neural network quantum states [58.720142291102135]
  We present a variational Monte Carlo method that solves the nuclear many-body problem in the occupation number formalism. A memory-efficient version of the reconfiguration algorithm is developed to train the network by minimizing the expectation value of the Hamiltonian.
  arXiv  Detail & Related papers  (2022-11-09T00:18:01Z)
• Hidden-nucleons neural-network quantum states for the nuclear many-body problem [0.0]
  We show that adding hidden nucleons to the original space augments the expressivity of the neural-network architecture. This method opens the way to highly-accurate quantum Monte Carlo studies of medium-mass nuclei.
  arXiv  Detail & Related papers  (2022-06-20T22:00:53Z)
• 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)
• Nuclei with up to $\boldsymbol{A=6}$ nucleons with artificial neural network wave functions [52.77024349608834]
  We use artificial neural networks to compactly represent the wave functions of nuclei. We benchmark their binding energies, point-nucleon densities, and radii with the highly accurate hyperspherical harmonics method.
  arXiv  Detail & Related papers  (2021-08-15T23:02:39Z)
• Controlled coherent dynamics of [VO(TPP)], a prototype molecular nuclear qudit with an electronic ancilla [50.002949299918136]
  We show that [VO(TPP)] (vanadyl tetraphenylporphyrinate) is a promising system suitable to implement quantum computation algorithms. It embeds an electronic spin 1/2 coupled through hyperfine interaction to a nuclear spin 7/2, both characterized by remarkable coherence.
  arXiv  Detail & Related papers  (2021-03-15T21:38:41Z)
• 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)
• Algorithmic Cooling of Nuclear Spin Pairs using a Long-Lived Singlet State [48.7576911714538]
  We show that significant cooling is achieved on an ensemble of spin-pair systems by exploiting the long-lived nuclear singlet state. This is the first demonstration of algorithmic cooling using a quantum superposition state.
  arXiv  Detail & Related papers  (2019-12-31T09:57:03Z)

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.