From the 1 of 10 linked papers with an AI index.
10 papers
Bridging Frustration and Non-Hermiticity via COMPASS: An Adaptive Biorthogonal Neural Quantum State Framework
Lavoisier Wah, Flore K. Kunst, Mohamed Hibat-Allah
The paper presents COMPASS, an adaptive biorthogonal neural quantum state method that combines autoregressive architectures with variational Monte Carlo to reliably find ground sta…
Parallel Scan Recurrent Neural Quantum States for Scalable Variational Monte Carlo
Ejaaz Merali, Mohamed Hibat-Allah, Mohammad Kohandel +2
Neural-network quantum states have emerged as a powerful variational framework for quantum many-body systems, with recent progress often driven by massively parallel architectures…
Geometry-Induced Long-Range Correlations in Recurrent Neural Network Quantum States
Asif Bin Ayub, Amine Mohamed Aboussalah, Mohamed Hibat-Allah
Neural Quantum States based on autoregressive recurrent neural network (RNN) wave functions enable efficient sampling without Markov-chain autocorrelation, but standard RNN archite…
Graph-Theoretic Analysis of Phase Optimization Complexity in Variational Wave Functions for Heisenberg Antiferromagnets
Mahmud Ashraf Shamim, Md Moshiur Rahman Raj, Mohamed Hibat-Allah +1
We study the computational complexity of learning the ground state phase structure of Heisenberg antiferromagnets. Representing Hilbert space as a weighted graph, the variational e…
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the triangular lattice
M. Schuyler Moss, Roeland Wiersema, Mohamed Hibat-Allah +2
Variational Monte Carlo simulations have been crucial for understanding quantum many-body systems, especially when the Hamiltonian is frustrated and the ground-state wavefunction h…
Leveraging recurrence in neural network wavefunctions for large-scale simulations of Heisenberg antiferromagnets on the square lattice
M. Schuyler Moss, Roeland Wiersema, Mohamed Hibat-Allah +2
Machine-learning-based variational Monte Carlo simulations are a promising approach for targeting quantum many-body ground states, especially in two dimensions and in cases where t…