462 citations · 4.5k across the 108 of their papers we have counts for
11 papers · 1 filter
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…
Quantum policy gradient algorithms
Sofiene Jerbi, Arjan Cornelissen, Māris Ozols +1
Understanding the power and limitations of quantum access to data in machine learning tasks is primordial to assess the potential of quantum computing in artificial intelligence. P…
Reduce&chop: Shallow circuits for deeper problems
Adrián Pérez-Salinas, Radoica Draškić, Jordi Tura +1
State-of-the-art quantum computers can only reliably execute circuits with limited qubit numbers and computational depth. This severely reduces the scope of algorithms that can be…
Analyzing Prospects for Quantum Advantage in Topological Data Analysis
Dominic W. Berry, Yuan Su, Casper Gyurik +7
Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers, a way to characterize topological features of data sets. Here, we propose, ana…
On establishing learning separations between classical and quantum machine learning with classical data
Casper Gyurik, Vedran Dunjko
Despite years of effort, the quantum machine learning community has only been able to show quantum learning advantages for certain contrived cryptography-inspired datasets in the c…
Reinforcement Learning Assisted Recursive QAOA
Yash J. Patel, Sofiene Jerbi, Thomas Bäck +1
Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices…
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets
Charles Moussa, Jan N. van Rijn, Thomas Bäck +1
As restricted quantum computers are slowly becoming a reality, the search for meaningful first applications intensifies. In this domain, one of the more investigated approaches is…
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…
Modern applications of machine learning in quantum sciences
Anna Dawid, Julian Arnold, Borja Requena +26
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learn…
High Dimensional Quantum Machine Learning With Small Quantum Computers
Simon C. Marshall, Casper Gyurik, Vedran Dunjko
Quantum computers hold great promise to enhance machine learning, but their current qubit counts restrict the realisation of this promise. In an attempt to placate this limitation…
Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm
Charles Moussa, Hao Wang, Thomas Bäck +1
As combinatorial optimization is one of the main quantum computing applications, many methods based on parameterized quantum circuits are being developed. In general, a set of para…