262 citations · 523 across the 19 of their papers we have counts for
19 papers
Quantum Geometric Tensor Preconditioning for Stable Training of Recurrent Neural Quantum States
Adil Attar, Amine M. Aboussalah, Mohamed Hibat-Allah
Neural Quantum States (NQS) provide a powerful neural network-based variational framework for representing many-body wave functions and solving for ground states. Recurrent Neural…
Bridging Frustration and Non-Hermiticity via COMPASS: An Adaptive Biorthogonal Neural Quantum State Framework
Lavoisier Wah, Flore K. Kunst, Mohamed Hibat-Allah
In this work, we introduce a complementary optimization method for progressive and adaptive state search (COMPASS) based on biorthogonal adaptive recurrent neural quantum states. O…
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 Formulation of Hamiltonian and Ground-State Sign Structures in Heisenberg Antiferromagnets
Mahmud Ashraf Shamim, Md Moshiur Rahman Raj, Mohamed Hibat-Allah +4
We study Heisenberg antiferromagnets through a graph-theoretic representation of their Hamiltonians. In the computational basis of a fixed-magnetization sector, the Heisenberg Hami…
Adaptive Neural Quantum States: A Recurrent Neural Network Perspective
Jake McNaughton, Mohamed Hibat-Allah
Neural-network quantum states (NQS) are powerful neural-network ansätzes that have emerged as promising tools for studying quantum many-body physics through the lens of the variati…
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…
Lattice Protein Folding with Variational Annealing
Shoummo Ahsan Khandoker, Estelle M. Inack, Mohamed Hibat-Allah
Understanding the principles of protein folding is a cornerstone of computational biology, with implications for drug design, bioengineering, and the understanding of fundamental b…
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…
Recurrent neural network wave functions for Rydberg atom arrays on kagome lattice
Mohamed Hibat-Allah, Ejaaz Merali, Giacomo Torlai +2
Rydberg atom array experiments have demonstrated the ability to act as powerful quantum simulators, preparing strongly-correlated phases of matter which are challenging to study fo…
A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models
Mohamed Hibat-Allah, Marta Mauri, Juan Carrasquilla +1
Generative modeling has seen a rising interest in both classical and quantum machine learning, and it represents a promising candidate to obtain a practical quantum advantage in th…
Investigating Topological Order using Recurrent Neural Networks
Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla
Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems.…
Variational Benchmarks for Quantum Many-Body Problems
Dian Wu, Riccardo Rossi, Filippo Vicentini +30
The continued development of computational approaches to many-body ground-state problems in physics and chemistry calls for a consistent way to assess its overall progress. In this…
Quantum HyperNetworks: Training Binary Neural Networks in Quantum Superposition
Juan Carrasquilla, Mohamed Hibat-Allah, Estelle Inack +4
Binary neural networks, i.e., neural networks whose parameters and activations are constrained to only two possible values, offer a compelling avenue for the deployment of deep lea…
Supplementing Recurrent Neural Network Wave Functions with Symmetry and Annealing to Improve Accuracy
Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla
Recurrent neural networks (RNNs) are a class of neural networks that have emerged from the paradigm of artificial intelligence and has enabled lots of interesting advances in the f…
Do Quantum Circuit Born Machines Generalize?
Kaitlin Gili, Mohamed Hibat-Allah, Marta Mauri +2
In recent proposals of quantum circuit models for generative tasks, the discussion about their performance has been limited to their ability to reproduce a known target distributio…
Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization Problems
Shoummo Ahsan Khandoker, Jawaril Munshad Abedin, Mohamed Hibat-Allah
Combinatorial optimization problems can be solved by heuristic algorithms such as simulated annealing (SA) which aims to find the optimal solution within a large search space throu…
Variational Neural Annealing
Mohamed Hibat-Allah, Estelle M. Inack, Roeland Wiersema +2
Many important challenges in science and technology can be cast as optimization problems. When viewed in a statistical physics framework, these can be tackled by simulated annealin…
Recurrent Neural Network Wave Functions
Mohamed Hibat-Allah, Martin Ganahl, Lauren E. Hayward +2
A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN). Its unique sequence-based architecture provides a tractable li…