462 citations · 4.5k across the 108 of their papers we have counts for
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Performance comparison of optimization methods on variational quantum algorithms
Xavier Bonet-Monroig, Hao Wang, Diederick Vermetten +5
Variational quantum algorithms (VQAs) offer a promising path toward using near-term quantum hardware for applications in academic and industrial research. These algorithms aim to f…
Quantum machine learning beyond kernel methods
Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup +3
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-term applications on noisy quantum computers. In this direction, various types of q…
LIMDD: A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States
Lieuwe Vinkhuijzen, Tim Coopmans, David Elkouss +2
Efficient methods for the representation and simulation of quantum states and quantum operations are crucial for the optimization of quantum circuits. Decision diagrams (DDs), a we…
Structural risk minimization for quantum linear classifiers
Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko
Quantum machine learning (QML) models based on parameterized quantum circuits are often highlighted as candidates for quantum computing's near-term ``killer application''. However,…
Reinforcement learning for optimization of variational quantum circuit architectures
Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk +2
The study of Variational Quantum Eigensolvers (VQEs) has been in the spotlight in recent times as they may lead to real-world applications of near-term quantum devices. However, th…
Experimental quantum speed-up in reinforcement learning agents
Valeria Saggio, Beate E. Asenbeck, Arne Hamann +10
Increasing demand for algorithms that can learn quickly and efficiently has led to a surge of development within the field of artificial intelligence (AI). An important paradigm wi…
Certificates of quantum many-body properties assisted by machine learning
Borja Requena, Gorka Muñoz-Gil, Maciej Lewenstein +2
Computationally intractable tasks are often encountered in physics and optimization. Such tasks often comprise a cost function to be optimized over a so-called feasible set, which…
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…
Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model
Saad Yalouz, Bruno Senjean, Filippo Miatto +1
Variational quantum algorithms (VQA) are considered as some of the most promising methods to determine the properties of complex strongly correlated quantum many-body systems, espe…
Parametrized quantum policies for reinforcement learning
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall +2
With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system i…
On solving classes of positive-definite quantum linear systems with quadratically improved runtime in the condition number
Davide Orsucci, Vedran Dunjko
Quantum algorithms for solving the Quantum Linear System (QLS) problem are among the most investigated quantum algorithms of recent times, with potential applications including the…