Cover Image

Symmetry-invariant quantum machine learning force fields

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Le, Isabel Nha Minh, Kiss, Oriel, Schuhmacher, Julian, Tavernelli, Ivano, Tacchino, Francesco
Format:	Preprint
Published:	2023
Subjects:	Quantum Physics Machine Learning Chemical Physics Computational Physics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Combining non-parametric quantum states and MERA tensor networks for ground-state optimization
by: Schuhmacher, Julian, et al.
Published: (2026)

Neutrino thermalization via randomization on a quantum processor
by: Kiss, Oriel, et al.
Published: (2025)

Optimizing two-dimensional isometric tensor networks with quantum computers
by: Leontica, Sebastian, et al.
Published: (2025)

Hybrid Tree Tensor Networks for quantum simulation
by: Schuhmacher, Julian, et al.
Published: (2024)

Accurate machine learning force fields via experimental and simulation data fusion
by: Röcken, Sebastien, et al.
Published: (2023)

Approximate quantum circuit compilation for proton-transfer kinetics on quantum processors
by: Kovyrshin, Arseny, et al.
Published: (2025)

Quantum chemistry with provable convergence via randomized sample-based Krylov quantum diagonalization
by: Piccinelli, Samuele, et al.
Published: (2025)

A circuit-differentiation framework for Green's functions on quantum computers
by: Piccinelli, Samuele, et al.
Published: (2025)

Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings
by: Schultz, Jonathan D., et al.
Published: (2025)

Quantum Embedding Method for the Simulation of Strongly Correlated Systems on Quantum Computers
by: Rossmannek, Max, et al.
Published: (2023)

Dual frame optimization for informationally complete quantum measurements
by: Fischer, Laurin E., et al.
Published: (2024)

Large-scale implementation of quantum subspace expansion with classical shadows
by: Fischer, Laurin E., et al.
Published: (2025)

On the design space between molecular mechanics and machine learning force fields
by: Wang, Yuanqing, et al.
Published: (2024)

The dark side of the forces: assessing non-conservative force models for atomistic machine learning
by: Bigi, Filippo, et al.
Published: (2024)

Grad DFT: a software library for machine learning enhanced density functional theory
by: Casares, Pablo A. M., et al.
Published: (2023)

A general framework for active space embedding methods: applications in quantum computing
by: Battaglia, Stefano, et al.
Published: (2024)

Generative flow-based warm start of the variational quantum eigensolver
by: Zou, Hang, et al.
Published: (2025)

Accurate quantum Monte Carlo forces for machine-learned force fields: Ethanol as a benchmark
by: Slootman, Emiel, et al.
Published: (2024)

From Vlasov-Poisson to Schrödinger-Poisson: dark matter simulation with a quantum variational time evolution algorithm
by: Cappelli, Luca, et al.
Published: (2023)

Probing the effects of broken symmetries in machine learning
by: Langer, Marcel F., et al.
Published: (2024)

Cartesian atomic cluster expansion for machine learning interatomic potentials
by: Cheng, Bingqing
Published: (2024)

Knowing when to trust machine-learned interatomic potentials
by: Mehdi, Shams, et al.
Published: (2026)

Enhancing molecular dynamics with equivariant machine-learned densities
by: Bogojeski, Mihail, et al.
Published: (2026)

Pushing the limits of unconstrained machine-learned interatomic potentials
by: Bigi, Filippo, et al.
Published: (2026)

Parametrized Quantum Circuit Learning for Quantum Chemical Applications
by: Jones, Grier M., et al.
Published: (2025)

The Convergence Frontier: Integrating Machine Learning and High Performance Quantum Computing for Next-Generation Drug Discovery
by: Ansari, Narjes, et al.
Published: (2026)

Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models
by: Arjona-Medina, Jose, et al.
Published: (2024)

Integrating Machine Learning and Quantum Circuits for Proton Affinity Predictions
by: Jin, Hongni, et al.
Published: (2024)

Predicting solvation free energies with an implicit solvent machine learning potential
by: Röcken, Sebastien, et al.
Published: (2024)

Simulating the Fermi-Hubbard model with long-range hopping on a quantum computer
by: Bespalova, Tatiana A., et al.
Published: (2024)

Engineered dissipation to mitigate barren plateaus
by: Sannia, Antonio, et al.
Published: (2023)

Representing spherical tensors with scalar-based machine-learning models
by: Domina, Michelangelo, et al.
Published: (2025)

Quantum-centric machine learning for molecular dynamics
by: Tao, Yanxian, et al.
Published: (2025)

Ab Initio Transcorrelated Method enabling accurate Quantum Chemistry on near-term Quantum Hardware
by: Dobrautz, Werner, et al.
Published: (2023)

Stabilizer-based quantum simulation of fermion dynamics with local qubit encodings
by: Gandon, Anthony, et al.
Published: (2025)

A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials
by: Kim, Dongjin, et al.
Published: (2025)

Extending machine learning model for implicit solvation to free energy calculations
by: Dey, Rishabh, et al.
Published: (2025)

Symmetry breaking in geometric quantum machine learning in the presence of noise
by: Tüysüz, Cenk, et al.
Published: (2024)

Understanding multi-fidelity training of machine-learned force-fields
by: Gardner, John L. A., et al.
Published: (2025)

Symmetry Dilemmas in Quantum Computing for Chemistry: A Comprehensive Analysis
by: Magoulas, Ilias, et al.
Published: (2025)