469 citations · 1.1k across the 8 of their papers we have counts for
9 papers
Quantum reinforcement learning of classical rare dynamics: Enhancement by intrinsic Fourier features
Alissa Wilms, Laura Ohff, Andrea Skolik +3
Rare events are essential for understanding the behavior of non-equilibrium and industrial systems. It is of ongoing interest to develop methods for effectively searching for rare…
Robustness of quantum reinforcement learning under hardware errors
Andrea Skolik, Stefano Mangini, Thomas Bäck +2
Variational quantum machine learning algorithms have become the focus of recent research on how to utilize near-term quantum devices for machine learning tasks. They are considered…
Equivariant quantum circuits for learning on weighted graphs
Andrea Skolik, Michele Cattelan, Sheir Yarkoni +2
Variational quantum algorithms are the leading candidate for advantage on near-term quantum hardware. When training a parametrized quantum circuit in this setting to solve a specif…
Hybrid quantum ResNet for car classification and its hyperparameter optimization
Asel Sagingalieva, Mo Kordzanganeh, Andrii Kurkin +6
Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of milli…
Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning
Andrea Skolik, Sofiene Jerbi, Vedran Dunjko
Quantum machine learning (QML) has been identified as one of the key fields that could reap advantages from near-term quantum devices, next to optimization and quantum chemistry. R…
Beating classical heuristics for the binary paint shop problem with the quantum approximate optimization algorithm
Michael Streif, Sheir Yarkoni, Andrea Skolik +2
The binary paint shop problem (BPSP) is an APX-hard optimization problem of the automotive industry. In this work, we show how to use the Quantum Approximate Optimization Algorithm…
Layerwise learning for quantum neural networks
Andrea Skolik, Jarrod R. McClean, Masoud Mohseni +2
With the increased focus on quantum circuit learning for near-term applications on quantum devices, in conjunction with unique challenges presented by cost function landscapes of p…
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
Matthew P. Harrigan, Kevin J. Sung, Matthew Neeley +83
We demonstrate the application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algor…
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
Michael Broughton, Guillaume Verdon, Trevor McCourt +26
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high…