arXiv:2606. 25600v1 Announce Type: cross Abstract: In the first part of this work, we construct the first type of two-dimensional (2D) hyperbolic neural quantum state (NQS) in the form of the Lorentz 2DRNN (Recurrent Neural Network) and benchmark its performance against the Euclidean 2DRNN in the paradigmatic $N\times N$ 2D Transverse Field Ising Model (2DTFIM) setting with different lattice sizes up to $N=12$ and at different transverse magnetic field strengths.
By H. L. Dao
arXiv:2604. 24337v3 Announce Type: replace-cross Abstract: In this work, we construct new non-Euclidean neural quantum states (NQS) based on hyperbolic Lorentz recurrent architectures (RNN/GRU).
By H. L. Dao
Quantum SEDONet is a quantum-enhanced deep operator network that embeds spectral features—Fourier for periodic coordinates and Chebyshev for bounded, non‑periodic coordinates—directly into the trunk network. This coordinate‑wise spectral embedding is achieved without adding qubits or circuit depth under unary amplitude encoding, and it reduces mean relative L2 error by up to 54.1% across four PDE benchmarks compared to the baseline Quantum DeepONet. The method demonstrates that quantum and classical inference paths agree to within 10⁻⁸, and it allows simultaneous use of both spectral bases within a single problem, as shown in a mixed‑boundary Poisson channel example.
By Muhammad Abid, Arth Sojitra, Bipin Tiwari, Omer San
arXiv:2604. 08763v2 Announce Type: replace-cross Abstract: We extend the Weak Adversarial Neural Pushforward Method to the Wigner transport equation governing the phase-space dynamics of quantum systems.
By Andrew Qing He, Wei Cai, Sihong Shao
arXiv:2609. 26032v1 Announce Type: cross Abstract: We construct the first type of non-Euclidean non-autoregressive neural quantum state (NQS) in the form of the hyperbolic Restricted Boltzmann Machine (HRBM), which is studied in the variational Monte-Carlo (VMC) setting of the Quantum Sherrington-Kirkpatrick (QSK) model whose ground state exhibits volume-law entanglement.
By H. L. Dao
arXiv:2604. 24337v2 Announce Type: replace-cross Abstract: In this work, we extend the class of previously introduced non-Euclidean neural quantum states (NQS) which consists only of Poincare hyperbolic GRU, to new variants including Poincare RNN as well as Lorentz RNN and Lorentz GRU.
By H. L. Dao
arXiv:2607. 01336v1 Announce Type: cross Abstract: Neural Quantum States (NQS) are a remarkably expressive class of variational ans\"atze for quantum many-body wavefunctions, yet little is understood about their internal mechanisms: trained on variational objectives alone, how do NQS accurately capture physical observables that they have never been explicitly optimized for?
By Zihao Qi, Christopher Earls
arXiv:2609.22189v1 Announce Type: cross
Abstract: The Schrodinger equation in one spatial dimension admits a small set of exactly solvable potentials that serve as natural proving grounds for any new...
By Tariq Mahmood, Waqas Arshad, Bilal Naseer, Alfredo Raya
arXiv:2512.12074v2 Announce Type: replace
Abstract: Physics-informed neural networks (PINNs) have emerged as an effective approach for solving partial differential equations (PDEs) by incorporating p...
By Gregorio P\'erez-Bernal, Oscar Rinc\'on-Carde\~no, Silvana Montoya-Noguera, Nicol\'as Guar\'in-Zapata
arXiv:2609. 40008v1 Announce Type: cross Abstract: Extracting the pion electromagnetic form factor $F_{\pi}(s)$ through phenomenological curve-fitting models introduces model dependence, unphysical artefacts, and kinematic inconsistencies.
By Mayank Goel, Subhadip Mitra, Monalisa Patra
arXiv:2606. 18713v1 Announce Type: new Abstract: Photonic quantum machine learning offers a route to trainable physical representations built from phase, interference and measurement.
By Jiale Linghu, Hao Dong, Yangshuai Wang
arXiv:2608. 05595v1 Announce Type: cross Abstract: Circuit cutting lets a large quantum neural network (QNN) run as independent subcircuits on small devices, but rebuilding its outputs by reconstruction carries a classical sampling overhead exponential in the number of cuts - the dominant runtime cost in prior work.
By Prabhjot Singh, Adel N. Toosi, Rajkumar Buyya