4 papers
Structure-Aware Transformers for Learning Near-Optimal Trotter Orderings with System-Size Generalization in 1D Heisenberg Hamiltonians
Shamminuj Aktar, Reuben Tate, Stephan Eidenbenz
Trotterization is a standard approach for simulating quantum time evolution on quantum computers, where the Hamiltonian is split into local terms and each term is applied in sequen…
An Analysis of Commutation-Based Trotter Ordering Strategies on Heisenberg-Style Hamiltonians
Reuben Tate, Shamminuj Aktar, Stephan Eidenbenz
Trotterization is a technique that allows one to approximate a time evolution of a Hamiltonian by repeatedly evolving the individual terms of the Hamiltonian one-at-a-time for smal…
Quantum Data Learning of Topological-to-Ferromagnetic Phase Transitions in the 2+1D Toric Code Loop Gas Model
Shamminuj Aktar, Rishabh Bhardwaj, Andreas Bärtschi +2
Quantum data learning (QDL) provides a framework for extracting physical insights directly from quantum states, bypassing the need for any identification of the classical observabl…
Quantum Graph Transformer for NLP Sentiment Classification
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +1
Quantum machine learning is a promising direction for building more efficient and expressive models, particularly in domains where understanding complex, structured data is critica…