5 papers
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…
tn4ml: Tensor Network Training and Customization for Machine Learning
Ema Puljak, Sergio Sanchez-Ramirez, Sergi Masot-Llima +3
Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their application…
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…