1 citations · 1 across the 6 of their papers we have counts for
6 papers
Setting angles in quantum approximate optimization at utility-scale
Maosheng Guo, Joel Jurado Diaz, Anurag Ramesh +16
The quantum approximate optimization algorithm (QAOA) is a powerful heuristic that seeks to solve combinatorial optimization problems using quantum hardware and classical optimizat…
Symplectic Neural Networks for Learning Non-Separable Hamiltonians
Harsh Choudhary, Vyacheslav Kungurtsev, Chandan Gupta +2
Hamiltonian Neural Networks (HNNs) integrate physical priors into neural models by learning a system's Hamiltonian, improving generalization and sample efficiency. Identifying the…
Binarizing Physics-Inspired GNNs for Combinatorial Optimization
Martin Krutský, Gustav Šír, Vyacheslav Kungurtsev +1
Physics-inspired graph neural networks (PI-GNNs) have been utilized as an efficient unsupervised framework for relaxing combinatorial optimization problems encoded through a specif…
Undecidable problems associated with variational quantum algorithms
Georgios Korpas, Vyacheslav Kungurtsev, Jakub Mareček
Variational Quantum Algorithms (VQAs), such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA), are widely studied as candidates…
ExMAG: Learning of Maximally Ancestral Graphs
Petr Ryšavý, Pavel Rytíř, Xiaoyu He +2
In mixed graphs, there are both directed and bidirected edges. An extension of acyclicity to this mixed-graph setting is known as maximally ancestral graphs. This extension is of c…
Learning Generalized Hamiltonians using fully Symplectic Mappings
Harsh Choudhary, Chandan Gupta, Vyacheslav Kungurtsev +2
Many important physical systems can be described as the evolution of a Hamiltonian system, which has the important property of being conservative, that is, energy is conserved thro…