6 papers
Experimental differentiation and extremization with analog quantum circuits
Evan Philip, Julius de Hond, Vytautas Abramavicius +8
Solving and optimizing differential equations (DEs) is ubiquitous in both engineering and fundamental science. The promise of quantum architectures to accelerate scientific computi…
Quantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning
Arthur M. Faria, Mehdi Djellabi, Igor O. Sokolov +1
We introduce Quantum Graph Attention Networks (QGATs) as trainable quantum encoders for inductive learning on graphs, extending the Quantum Graph Neural Networks (QGNN) framework.…
Materials Discovery With Quantum-Enhanced Machine Learning Algorithms
Ignacio F. Graña, Savvas Varsamopoulos, Tatsuhito Ando +2
Materials discovery is a computationally intensive process that requires exploring vast chemical spaces to identify promising candidates with desirable properties. In this work, we…
Inductive Graph Representation Learning with Quantum Graph Neural Networks
Arthur M. Faria, Ignacio F. Graña, Savvas Varsamopoulos
Quantum Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs)…
Decoding surface code with a distributed neural network based decoder
Savvas Varsamopoulos, Koen Bertels, Carmen G. Almudever
There has been a rise in decoding quantum error correction codes with neural network based decoders, due to the good decoding performance achieved and adaptability to any noise mod…
Comparing neural network based decoders for the surface code
Savvas Varsamopoulos, Koen Bertels, Carmen G. Almudever
Matching algorithms can be used for identifying errors in quantum systems, being the most famous the Blossom algorithm. Recent works have shown that small distance quantum error co…