The variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithms for near-term quantum computers. Meanwhile, VQE is …
Calculating excited-state properties of molecules and solids is one of the main computational challenges of modern electronic structure theory. By combining and …
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 …
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 …
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 …
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 …