49 citations · 57 across the 12 of their papers we have counts for
13 papers
Learning Trotter Orderings for Heisenberg Hamiltonians with a Ranking Transformer
Shamminuj Aktar, Reuben Tate, Stephan Eidenbenz
Trotterization approximates quantum time evolution by sequentially applying Hamiltonian terms. Because noncommuting terms introduce ordering-dependent errors, selecting an optimal…
Learning Low-Energy Subspace Overlaps in Many-Body Systems with Measurement-Based and Coherent Quantum Strategies
Shamminuj Aktar, Rishabh Bhardwaj, Tanmoy Bhattacharya +1
Predicting the overlap of quantum states with specified low-energy subspaces is a key diagnostic for quantum many-body dynamics, with direct applications in state preparation, subs…
The Quantum Hamiltonian Analysis Toolkit: Lowering the Barrier to Quantum Computing with Hamiltonians
Brendan K. Krueger, Stephan Eidenbenz, Shamminuj Aktar +8
We present the Quantum Hamiltonian Analysis Toolkit (QHAT), a newly developed application that provides a user-friendly interface for studying Hamiltonians and performing Hamiltoni…
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…
Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
Shamminuj Aktar, Andreas Bärtschi, Diane Oyen +2
Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs…
Predicting Expressibility of Parameterized Quantum Circuits using Graph Neural Network
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +2
Parameterized Quantum Circuits (PQCs) are essential to quantum machine learning and optimization algorithms. The expressibility of PQCs, which measures their ability to represent a…
Scalable Experimental Bounds for Entangled Quantum State Fidelities
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +1
Estimating the state preparation fidelity of highly entangled states on noisy intermediate-scale quantum (NISQ) devices is important for benchmarking and application considerations…
Quantum Netlist Compiler (QNC)
Shamminuj Aktar, Abdel-Hameed A. Badawy, Nandakishore Santhi
Over the last decade, Quantum Computing hardware has rapidly developed and become a very intriguing, promising, and active research field among scientists worldwide. To achieve the…
BB-ML: Basic Block Performance Prediction using Machine Learning Techniques
Hamdy Abdelkhalik, Shamminuj Aktar, Yehia Arafa +8
Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to…
A Divide-and-Conquer Approach to Dicke State Preparation
Shamminuj Aktar, Andreas Bärtschi, Abdel-Hameed A. Badawy +1
We present a divide-and-conquer approach to deterministically prepare Dicke states (i.e., equal-weight superpositions of all -qubit states with Hamming Wei…