54 citations · 190 across the 8 of their papers we have counts for
8 papers
Compilation of product-formula Hamiltonian simulation via reinforcement learning
Lea M. Trenkwalder, Eleanor Scerri, Thomas E. O'Brien +1
Hamiltonian simulation is believed to be one of the first tasks where quantum computers can yield a quantum advantage. One of the most popular methods of Hamiltonian simulation is…
Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning
Francesco Preti, Michael Schilling, Sofiene Jerbi +4
Shortening quantum circuits is crucial to reducing the destructive effect of environmental decoherence and enabling useful algorithms. Here, we demonstrate an improvement in such c…
Automated Gadget Discovery in Science
Lea M. Trenkwalder, Andrea López Incera, Hendrik Poulsen Nautrup +2
In recent years, reinforcement learning (RL) has become increasingly successful in its application to science and the process of scientific discovery in general. However, while RL…
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…
Operationally meaningful representations of physical systems in neural networks
Hendrik Poulsen Nautrup, Tony Metger, Raban Iten +5
To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems. The representations learnt by most…
Quantum enhancements for deep reinforcement learning in large spaces
Sofiene Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup +2
In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorit…
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes
Walter L. Boyajian, Jens Clausen, Lea M. Trenkwalder +2
In recent years, the interest in leveraging quantum effects for enhancing machine learning tasks has significantly increased. Many algorithms speeding up supervised and unsupervise…
Photonic architecture for reinforcement learning
Fulvio Flamini, Arne Hamann, Sofiène Jerbi +3
The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with…