Predicting energy of the quantum system from one- and two- electron integrals using Deep Learning

• URL: http://arxiv.org/abs/2504.03849v1
• Date: Fri, 04 Apr 2025 18:22:17 GMT
• Title: Predicting energy of the quantum system from one- and two- electron integrals using Deep Learning
• Authors: Valerii Chuiko, Paul W. Ayers,
• Abstract summary: We train a neural network to predict the energies of strongly-correlated systems.<n>Because our network uses and preserves size-consistency, training on few-electron systems can guide predictions for systems with more electrons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a descriptor for molecular electronic structure that is based solely on the one- and two-electron integrals but is translationally, rotationally, and unitarily invariant. Then, directly exploiting size consistency, we train and then fine-tune a neural network to predict the energies of strongly-correlated systems, specifically hydrogen clusters. Because our network uses and preserves size-consistency, training on few-electron systems can guide predictions for systems with more electrons.

Related papers

• Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems [52.19558333652367]
  We present finite-range embeddings (FiRE) for accurate large-scale ab-initio electronic structure calculations. FiRE reduces the complexity of neural-network variational Monte Carlo (NN-VMC) by $sim ntextel$, the number of electrons. We validate our method's accuracy on various challenging systems, including biochemical compounds and organometallic compounds.
  arXiv  Detail & Related papers  (2025-04-08T14:28:54Z)
• Quantum Electrodynamics Coupled-Cluster Theory: Exploring Photon-Induced Electron Correlations [0.1666604949258699]
  We present our successful implementation of the quantum electrodynamics coupled-cluster method with single and double excitations. We discuss theoretical foundations, algorithmic details, and numerical benchmarks to demonstrate how the integration of bosonic degrees of freedom alters the electronic ground state.
  arXiv  Detail & Related papers  (2024-09-10T20:48:47Z)
• Neural Pfaffians: Solving Many Many-Electron Schrödinger Equations [58.130170155147205]
  Neural wave functions accomplished unprecedented accuracies in approximating the ground state of many-electron systems, though at a high computational cost. Recent works proposed amortizing the cost by learning generalized wave functions across different structures and compounds instead of solving each problem independently. This work tackles the problem by defining overparametrized, fully learnable neural wave functions suitable for generalization across molecules.
  arXiv  Detail & Related papers  (2024-05-23T16:30:51Z)
• Physical Entanglement Between Localized Orbitals [1.3812010983144802]
  In [arXiv:2207.03377] the first closed formula of a faithful entanglement measure applicable to realistic electron systems has been derived. We build on this key achievement with the ultimate goal of guiding the development of quantum technologies.
  arXiv  Detail & Related papers  (2023-03-24T17:32:06Z)
• A Hybrid Quantum-Classical Method for Electron-Phonon Systems [40.80274768055247]
  We develop a hybrid quantum-classical algorithm suitable for this type of correlated systems. This hybrid method tackles with arbitrarily strong electron-phonon coupling without increasing the number of required qubits and quantum gates. We benchmark the new method by applying it to the paradigmatic Hubbard-Holstein model at half filling, and show that it correctly captures the competition between charge density wave and antiferromagnetic phases.
  arXiv  Detail & Related papers  (2023-02-20T08:08:51Z)
• Reducing the Quantum Many-electron Problem to Two Electrons with Machine Learning [0.0]
  We introduce a new paradigm for electronic structure where approximate geminal-occupation distributions are learned'' via a convolutional neural network. We show that the neural network learns the $N$-representability conditions, constraints on the distribution for it to represent an $N$-electron system.
  arXiv  Detail & Related papers  (2022-12-29T16:30:37Z)
• A Self-Attention Ansatz for Ab-initio Quantum Chemistry [3.4161707164978137]
  We present a novel neural network architecture using self-attention, the Wavefunction Transformer (Psiformer) We show that the Psiformer can be used as a drop-in replacement for other neural networks, often dramatically improving the accuracy of the calculations. This demonstrates that self-attention networks can learn complex quantum mechanical correlations between electrons, and are a promising route to reaching unprecedented accuracy in chemical calculations on larger systems.
  arXiv  Detail & Related papers  (2022-11-24T15:38:55Z)
• Neural-Network Quantum States for Periodic Systems in Continuous Space [66.03977113919439]
  We introduce a family of neural quantum states for the simulation of strongly interacting systems in the presence of periodicity. For one-dimensional systems we find very precise estimations of the ground-state energies and the radial distribution functions of the particles. In two dimensions we obtain good estimations of the ground-state energies, comparable to results obtained from more conventional methods.
  arXiv  Detail & Related papers  (2021-12-22T15:27:30Z)
• Nuclear two point correlation functions on a quantum-computer [105.89228861548395]
  We use current quantum hardware and error mitigation protocols to calculate response functions for a highly simplified nuclear model. In this work we use current quantum hardware and error mitigation protocols to calculate response functions for a modified Fermi-Hubbard model in two dimensions with three distinguishable nucleons on four lattice sites.
  arXiv  Detail & Related papers  (2021-11-04T16:25:33Z)
• 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)
• Entangling nuclear spins in distant quantum dots via an electron bus [0.0]
  The protocol relies on the injection of electrons with definite polarization in each quantum dot and the coherent transfer of electrons from one quantum dot to the other. We are able to confirm that our protocol indeed produces entanglement for both homogeneous and inhomogeneous systems.
  arXiv  Detail & Related papers  (2020-12-17T11:15:39Z)
• Entanglement generation via power-of-SWAP operations between dynamic electron-spin qubits [62.997667081978825]
  Surface acoustic waves (SAWs) can create moving quantum dots in piezoelectric materials. We show how electron-spin qubits located on dynamic quantum dots can be entangled.
  arXiv  Detail & Related papers  (2020-01-15T19:00:01Z)

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.