Saved in:
| Main Authors: | Torabian, Elham, Krems, Roman V. |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.05955 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Lattice stitching by eigenvector continuation for Holstein polaron
by: Torabian, Elham, et al.
Published: (2025)
by: Torabian, Elham, et al.
Published: (2025)
Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms
by: Kairon, Pranav, et al.
Published: (2025)
by: Kairon, Pranav, et al.
Published: (2025)
Benchmarking of quantum fidelity kernels for Gaussian process regression
by: Guo, Xuyang, et al.
Published: (2024)
by: Guo, Xuyang, et al.
Published: (2024)
Advantage of discrete variable representation in variational quantum eigensolvers for vibrational energy calculations
by: Asnaashari, K., et al.
Published: (2023)
by: Asnaashari, K., et al.
Published: (2023)
Extrapolation of polaron properties to low phonon frequencies by Bayesian machine learning
by: Kairon, Pranav, et al.
Published: (2023)
by: Kairon, Pranav, et al.
Published: (2023)
Quantum feature-map learning with reduced resource overhead
by: Jäger, Jonas, et al.
Published: (2025)
by: Jäger, Jonas, et al.
Published: (2025)
Invariance of quantum scattering rate coefficients to anisotropy of atom-molecule interactions
by: Guo, Xuyang, et al.
Published: (2025)
by: Guo, Xuyang, et al.
Published: (2025)
Quantum Gaussian process model of potential energy surface for a polyatomic molecule
by: Dai, Jun, et al.
Published: (2022)
by: Dai, Jun, et al.
Published: (2022)
VQE-generated quantum circuit dataset for machine learning
by: Nakayama, Akimoto, et al.
Published: (2023)
by: Nakayama, Akimoto, et al.
Published: (2023)
Hypergraphic representation for adaptive quantum circuits
by: Cambiucci, Waldemir, et al.
Published: (2025)
by: Cambiucci, Waldemir, et al.
Published: (2025)
A no free lunch theorem for untrained quantum circuits in machine learning
by: Herbert, Steven
Published: (2023)
by: Herbert, Steven
Published: (2023)
Dimension reduction with structure-aware quantum circuits for hybrid machine learning
by: Daskin, Ammar
Published: (2025)
by: Daskin, Ammar
Published: (2025)
Modern applications of machine learning in quantum sciences
by: Dawid, Anna, et al.
Published: (2022)
by: Dawid, Anna, et al.
Published: (2022)
Prospects for quantum advantage in machine learning from the representability of functions
by: Masot-Llima, Sergi, et al.
Published: (2025)
by: Masot-Llima, Sergi, et al.
Published: (2025)
Minimizing the negativity of quantum circuits in overcomplete quasiprobability representations
by: Kulikov, Denis A., et al.
Published: (2023)
by: Kulikov, Denis A., et al.
Published: (2023)
Simulation of noisy quantum circuits using frame representations
by: Denzler, Janek, et al.
Published: (2026)
by: Denzler, Janek, et al.
Published: (2026)
Error-mitigated photonic quantum circuit Born machine
by: Salavrakos, Alexia, et al.
Published: (2024)
by: Salavrakos, Alexia, et al.
Published: (2024)
Rigorous quantum calculations for atom-molecule chemical reactions in electric fields: from single to multiple partial wave regimes
by: Tscherbul, Timur V., et al.
Published: (2025)
by: Tscherbul, Timur V., et al.
Published: (2025)
Variational learning of integrated quantum photonic circuits
by: Zhang, Hui, et al.
Published: (2024)
by: Zhang, Hui, et al.
Published: (2024)
Hierarchical quantum circuit representations for neural architecture search
by: Lourens, Matt, et al.
Published: (2022)
by: Lourens, Matt, et al.
Published: (2022)
Sparse identification of quantum Hamiltonian dynamics via quantum circuit learning
by: Tateyama, Yusei, et al.
Published: (2026)
by: Tateyama, Yusei, et al.
Published: (2026)
Exploring quantum localization with machine learning
by: Montes, J., et al.
Published: (2024)
by: Montes, J., et al.
Published: (2024)
Low-density parity-check representation of fault-tolerant quantum circuits
by: Li, Ying
Published: (2024)
by: Li, Ying
Published: (2024)
Efficient quantum circuits for high-dimensional representations of SU(n) and Ramanujan quantum expanders
by: Iyer, Vishnu, et al.
Published: (2026)
by: Iyer, Vishnu, et al.
Published: (2026)
Local surrogates for quantum machine learning
by: Nair, Sreeraj Rajindran, et al.
Published: (2025)
by: Nair, Sreeraj Rajindran, et al.
Published: (2025)
Measurement-based quantum machine learning
by: Calderón, Luis Mantilla, et al.
Published: (2024)
by: Calderón, Luis Mantilla, et al.
Published: (2024)
Single-shot quantum machine learning
by: Recio-Armengol, Erik, et al.
Published: (2024)
by: Recio-Armengol, Erik, et al.
Published: (2024)
Research progress on quantum neural networks and quantum machine learning
by: Sun, Yifan, et al.
Published: (2026)
by: Sun, Yifan, et al.
Published: (2026)
General classification of qubit encodings in ultracold diatomic molecules
by: Asnaashari, K., et al.
Published: (2023)
by: Asnaashari, K., et al.
Published: (2023)
Echoes in a parametrically perturbed Kerr-nonlinear oscillator
by: Mao, Yun-Wen, et al.
Published: (2025)
by: Mao, Yun-Wen, et al.
Published: (2025)
Boundaries of universality of thermal collisions for atom-atom scattering
by: Guo, Xuyang, et al.
Published: (2024)
by: Guo, Xuyang, et al.
Published: (2024)
Scalable projected entangled-pair state representation of random quantum circuit states
by: Lee, Sung-Bin B., et al.
Published: (2025)
by: Lee, Sung-Bin B., et al.
Published: (2025)
Nanodiamond quantum thermometry assisted with machine learning
by: Yamamoto, Kouki, et al.
Published: (2025)
by: Yamamoto, Kouki, et al.
Published: (2025)
Black hole/quantum machine learning correspondence
by: Lee, Jae-Weon, et al.
Published: (2025)
by: Lee, Jae-Weon, et al.
Published: (2025)
Can machine learning for quantum-gas experiments be explainable?
by: Zwolak, I. B. Spielman amd J. P.
Published: (2026)
by: Zwolak, I. B. Spielman amd J. P.
Published: (2026)
Encoding molecular structures in quantum machine learning
by: Boy, Choy, et al.
Published: (2025)
by: Boy, Choy, et al.
Published: (2025)
The power of entanglement in distributed quantum machine learning
by: Kim, Yerim, et al.
Published: (2026)
by: Kim, Yerim, et al.
Published: (2026)
A Kerr kernel quantum learning machine
by: Wood, Carolyn, et al.
Published: (2024)
by: Wood, Carolyn, et al.
Published: (2024)
Shadows of quantum machine learning
by: Jerbi, Sofiene, et al.
Published: (2023)
by: Jerbi, Sofiene, et al.
Published: (2023)
Quantum machine learning for the quantum lattice Boltzmann method: Trainability of variational quantum circuits for the nonlinear collision operator across multiple time steps
by: Zamora, Antonio David Bastida, et al.
Published: (2026)
by: Zamora, Antonio David Bastida, et al.
Published: (2026)
Similar Items
-
Lattice stitching by eigenvector continuation for Holstein polaron
by: Torabian, Elham, et al.
Published: (2025) -
Equivalence between exponential concentration in quantum machine learning kernels and barren plateaus in variational algorithms
by: Kairon, Pranav, et al.
Published: (2025) -
Benchmarking of quantum fidelity kernels for Gaussian process regression
by: Guo, Xuyang, et al.
Published: (2024) -
Advantage of discrete variable representation in variational quantum eigensolvers for vibrational energy calculations
by: Asnaashari, K., et al.
Published: (2023) -
Extrapolation of polaron properties to low phonon frequencies by Bayesian machine learning
by: Kairon, Pranav, et al.
Published: (2023)