5 papers · 1 filter
Limits of Clifford Disentangling in Tensor Network States
Sergi Masot-Llima, Piotr Sierant, Paolo Stornati +1
Tensor network methods leverage the limited entanglement of quantum states to efficiently simulate many-body systems. Alternatively, Clifford circuits provide a framework for handl…
Prospects for quantum advantage in machine learning from the representability of functions
Sergi Masot-Llima, Elies Gil-Fuster, Carlos Bravo-Prieto +2
Demonstrating quantum advantage in machine learning tasks requires navigating a complex landscape of proposed models and algorithms. To bring clarity to this search, we introduce a…
Advantages of density in tensor network geometries for gradient based training
Sergi Masot-Llima, Artur Garcia-Saez
Tensor networks are a very powerful data structure tool originating from quantum system simulations. In recent years, they have seen increased use in machine learning, mostly in tr…
Entropy-driven entanglement forging
Axel Pérez-Obiol, Sergi Masot-Llima, Antonio M. Romero +4
Simulating physical systems with variational quantum algorithms is a well-studied approach, but it is challenging to implement in current devices due to demands in qubit number and…
Stabilizer Tensor Networks: universal quantum simulator on a basis of stabilizer states
Sergi Masot-Llima, Artur Garcia-Saez
Efficient simulation of quantum computers relies on understanding and exploiting the properties of quantum states. This is the case for methods such as tensor networks, based on en…