1 citations · 5 across the 6 of their papers we have counts for
6 papers
Toward Quantum-Aware Machine Learning: Improved Prediction of Quantum Dissipative Dynamics via Complex Valued Neural Networks
Muhammad Atif, Arif Ullah, Ming Yang
Accurately modeling quantum dissipative dynamics remains challenging due to environmental complexity and non-Markovian memory effects. Although machine learning provides a promisin…
Quantum-inspired Chemical Rule for Discovering Topological Materials
Xinyu Xu, Rajibul Islam, Ghulam Hussain +5
Topological materials exhibit unique electronic structures that underpin both fundamental quantum phenomena and next-generation technologies, yet their discovery remains constraine…
TXL Fusion: A Hybrid Machine Learning Framework Integrating Chemical Heuristics and Large Language Models for Topological Materials Discovery
Arif Ullah, Rajibul Islam, Yangming Huang +4
Topological materials, including topological insulators (TIs) and topological semimetals (TSMs), offer promising platforms for quantum, spintronic, and low-dissipation electronic t…
From short-sighted to far-sighted: A comparative study of recursive machine learning approaches for open quantum systems
Arif Ullah
Accurately modeling open quantum system dynamics is crucial for advancing quantum technologies, yet traditional methods struggle to balance accuracy and efficiency. Machine learnin…
Digital stabilization of an IQ modulator in the carrier suppressed single side-band (CS-SSB) mode for atom interferometry
Arif Ullah, Samuel Legge, John D. Close +2
We present an all-digital method for stabilising the phase biases in an electro-optic I/Q modulator for carrier-suppressed single-sideband modulation. Building on the method presen…
Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
Arif Ullah, Yu Huang, Ming Yang +1
Neural networks (NNs) accelerate simulations of quantum dissipative dynamics. Ensuring that these simulations adhere to fundamental physical laws is crucial, but has been largely i…