117 citations · 198 across the 8 of their papers we have counts for
9 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.…
Conservative quantum offline model-based optimization
Kristian Sotirov, Annie E. Paine, Savvas Varsamopoulos +2
Offline model-based optimization (MBO) refers to the task of optimizing a black-box objective function using only a fixed set of prior input-output data, without any active experim…
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
Graph Neural Networks (QGNNs) offer a promising approach to combining quantum computing with graph-structured data processing. While classical Graph Neural Networks (GNNs) are scal…
Quantum Extremal Learning
Savvas Varsamopoulos, Evan Philip, Herman W. T. van Vlijmen +6
We propose a quantum algorithm for `extremal learning', which is the process of finding the input to a hidden function that extremizes the function output, without having direct ac…
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…
Decoding Small Surface Codes with Feedforward Neural Networks
Savvas Varsamopoulos, Ben Criger, Koen Bertels
Surface codes reach high error thresholds when decoded with known algorithms, but the decoding time will likely exceed the available time budget, especially for near-term implement…