Molecular quantum circuit design: A graph-based approach
JS Kottmann - Quantum, 2023 - quantum-journal.org
Science is rich in abstract concepts that capture complex processes in astonishingly simple
ways. A prominent example is the reduction of molecules to simple graphs. This work …
ways. A prominent example is the reduction of molecules to simple graphs. This work …
Classically optimized variational quantum eigensolver with applications to topological phases
The variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid
quantum-classical algorithms for near-term quantum computers. Meanwhile, VQE is …
quantum-classical algorithms for near-term quantum computers. Meanwhile, VQE is …
Quantum parallelized variational quantum eigensolvers for excited states
Calculating excited-state properties of molecules and solids is one of the main
computational challenges of modern electronic structure theory. By combining and …
computational challenges of modern electronic structure theory. By combining and …
Hybrid Quantum Graph Neural Network for Molecular Property Prediction
M Vitz, H Mohammadbagherpoor, S Sandeep… - arXiv preprint arXiv …, 2024 - arxiv.org
To accelerate the process of materials design, materials science has increasingly used data
driven techniques to extract information from collected data. Specially, machine learning …
driven techniques to extract information from collected data. Specially, machine learning …
Adaptive Quantum Generative Training using an Unbounded Loss Function
K Sherbert, J Furches, K Shirali, SE Economou… - arXiv preprint arXiv …, 2024 - arxiv.org
We propose a generative quantum learning algorithm, R\'enyi-ADAPT, using the Adaptive
Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss …
Derivative-Assembled Problem Tailored ansatz (ADAPT) framework in which the loss …
Evaluating the efficiency of ground-state-preparation algorithms
K Gratsea, C Sun, PD Johnson - Physical Review A, 2024 - APS
In recent years, substantial research effort has been devoted to quantum algorithms for
ground-state-energy estimation (GSEE) in chemistry and materials. Given the many heuristic …
ground-state-energy estimation (GSEE) in chemistry and materials. Given the many heuristic …
Quantum Neural Networks: Issues, Training, and Applications
Our work in the field aims at explaining the limitations and expressive power of Quantum
Machine Learning models, as well as finding feasible training algorithms that could be …
Machine Learning models, as well as finding feasible training algorithms that could be …