Carrasquilla, Juan

Machine learning phases of matter

Carrasquilla, J., & Melko, R. G. (2017) · Nature Physics

Quantum many-body systemsMachine Learning in Materials Science

Cited by 1,448

Recurrent neural network wave functions

Hibat-Allah, M., Ganahl, M., Hayward, L. E., Melko, R. G., & Carrasquilla, J. (2020) · Physical Review Research

Quantum many-body systemsQuantum Computing Algorithms and Architecture

Cited by 274

Beyond-classical computation in quantum simulation

King, A. D., Nocera, A., Rams, M. M., Dziarmaga, J., Wiersema, R., Bernoudy, W., Raymond, J., Kaushal, N., Heinsdorf, N., Harris, R., et al. (2025) · Science

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 138

Variational benchmarks for quantum many-body problems

Wu, D., Rossi, R., Vicentini, F., Astrakhantsev, N., Becca, F., Cao, X., Carrasquilla, J., Ferrari, F., Georges, A., Hibat-Allah, M., et al. (2024) · Science

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 79

Probabilistic simulation of quantum circuits using a deep-learning architecture

Carrasquilla, J., Luo, D., Pérez, F., Milsted, A., Clark, B. K., Volkovs, M., & Aolita, L. (2021) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 53

Investigating topological order using recurrent neural networks

Hibat-Allah, M., Melko, R. G., & Carrasquilla, J. (2023) · Physical Review B

Quantum many-body systemsQuantum Computing Algorithms and Architecture

Cited by 27

Neural network approach to quasiparticle dispersions in doped antiferromagnets

Lange, H., Döschl, F., Carrasquilla, J., & Bohrdt, A. (2024) · Communications Physics

Quantum many-body systemsPhysics of Superconductivity and Magnetism

Cited by 18