1.1k citations · 7.3k across the 119 of their papers we have counts for
118 papers
Digital-Analog Quantum Simulation and Computing: A Perspective on Past and Future Developments
Lucas Lamata
Quantum simulation and computing traditionally has been based on two main paradigms, namely, digital and analog. In the digital paradigm, usually single and two-qubit gates (where…
Quantum memristors for neuromorphic quantum machine learning
Lucas Lamata
Quantum machine learning may permit to realize more efficient machine learning calculations with near-term quantum devices. Among the diverse quantum machine learning paradigms whi…
Benefits of Open Quantum Systems for Quantum Machine Learning
María Laura Olivera-Atencio, Lucas Lamata, Jesús Casado-Pascual
Quantum machine learning is a discipline that holds the promise of revolutionizing data processing and problem-solving. However, dissipation and noise arising from the coupling wit…
Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning
J. E. García-Ramos, A. Sáiz, J. M. Arias +2
In this paper, the application of quantum simulations and quantum machine learning to solve low-energy nuclear physics problems is explored. The use of quantum computing to deal wi…
Digital-Analog Quantum Computation with Arbitrary Two-Body Hamiltonians
Mikel Garcia-de-Andoin, Álvaro Saiz, Pedro Pérez-Fernández +3
Digital-analog quantum computing is a computational paradigm which employs an analog Hamiltonian resource together with single-qubit gates to reach universality. Here, we design a…
Quantum Machine Learning Implementations: Proposals and Experiments
Lucas Lamata
This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning. Without an aim to…