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Giuseppe Carleo

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Giuseppe Carleo
Giuseppe Carleo in 2021
Born1984 (age 39–40)
CitizenshipItalian
Known forNeural network quantum states
Time-dependent variational Monte Carlo
Academic background
EducationPhysics
Alma materSapienza University of Rome
International School for Advanced Studies
ThesisSpectral and dynamical properties of strongly correlated systems (2011)
Doctoral advisorStefano Baroni
Other advisorsMatthias Troyer
Academic work
DisciplinePhysics
Sub-disciplineComputational physics
InstitutionsEPFL (École Polytechnique Fédérale de Lausanne)
Main interestsMachine learning
Quantum computing
Condensed matter physics
Websitehttps://www.epfl.ch/labs/cqsl/

Giuseppe Carleo (born 1984) is an Italian physicist. He is a professor of computational physics at EPFL (École Polytechnique Fédérale de Lausanne) and the head of the Laboratory of Computational Quantum Science.[1][2]

Career

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Carleo studied physics at the Sapienza University of Rome and in 2011 earned his PhD in theoretical physics at the International School for Advanced Studies under the supervision of Stefano Baroni. His thesis on "Spectral and dynamical properties of strongly correlated systems" was dedicated to novel numerical simulation techniques to study condensed-matter systems, such as the time-dependent variational Monte Carlo.[3]

As a Marie Curie Fellow he joined the École supérieure d'optique to work in the Lab directed by Alain Aspect on theoretically model and simulate ultra-cold atoms systems.[4] In 2015, he went to work with the group of Matthias Troyer at the ETH Zurich where he later became a lecturer of computational quantum physics.[5][6] Here he investigated the idea of representing complex quantum systems using artificial neural networks and machine learning techniques, developing a family of variational states known as neural network quantum states. In 2018, as research scientist and project leader he joined the Center for Computational Quantum Physics at Flatiron Institute of the Simons Foundation in New York City.[7] Here he became a member of a team of researchers developing numerical methods at the intersection of machine learning and quantum science.[8][9] Since 2018 he has been leading the open-source project NetKet.[10]

Since 2020 he has been a professor of quantum computing at EPFL and the head of the Laboratory of Computational Quantum Science at the EPFL's School of Basic Sciences.[1][2][11]

Research

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Carleo's main focus is the development of methods in computational science to study challenging problems involving strongly interacting quantum systems and quantum computing.

In 2016, he introduced a representation of many-particle quantum wave functions based on artificial neural networks. This approach is known as neural network quantum states[12] and constitutes one of the early applications of machine learning techniques in modern many-body quantum physics. An application of this representation[13] is for example used for quantum tomography of interacting Rydberg atoms.[14]

In 2011, he also co-developed the time-dependent variational Monte Carlo method,[15] a technique to simulate the dynamics of quantum systems using variational Monte Carlo. This approach is used for example to simulate the dynamics of two-dimensional interacting quantum models.[16][17]

Carleo has also contributed to the development of quantum algorithms, especially in the context of variational quantum simulation.[18]

His research has been featured in news outlets such as New Scientist,[19] Ars Technica,[20] Physics World,[21] Chemistry World,[22] and Vice.[23] Some of his lectures are also available on YouTube.[24][25]

Distinctions

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He is a scholar at the ELLIS Society (since 2020)[26] and a member of the editorial board of Machine Learning Science and Technology (since 2019).[27]

Selected works

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References

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  1. ^ a b "People". www.epfl.ch. Retrieved 2021-04-09.
  2. ^ a b "11 new professors appointed at the two Federal Institutes of Technology | ETH-Board". www.ethrat.ch. Archived from the original on 2021-08-18. Retrieved 2021-04-09.
  3. ^ "Spectral and dynamical properties of strongly correlated systems". iris.sissa.it. Retrieved 2021-04-09.
  4. ^ "Quantum Dynamics of Strongly Correlated Systems and Ultra-Cold Atomic Gases | MASCARA Project". Retrieved 2021-05-12.
  5. ^ "Course Catalogue - ETH Zurich". Retrieved 2021-05-12.
  6. ^ "Maschinelles Lernen: Neuronale Netze als Quantensimulator". www.spektrum.de (in German). Retrieved 2021-05-17.
  7. ^ "Giuseppe Carleo". Simons Foundation. 2018-02-05. Retrieved 2021-04-21.
  8. ^ Siegfried, Tom (2020-08-27). "Why some artificial intelligence is smart until it's dumb". Knowable Magazine. doi:10.1146/knowable-082720-1. S2CID 225302152.
  9. ^ Carleo, Giuseppe; Cirac, Ignacio; Cranmer, Kyle; Daudet, Laurent; Schuld, Maria; Tishby, Naftali; Vogt-Maranto, Leslie; Zdeborová, Lenka (2019-12-06). "Machine learning and the physical sciences". Reviews of Modern Physics. 91 (4): 045002. arXiv:1903.10563. Bibcode:2019RvMP...91d5002C. doi:10.1103/RevModPhys.91.045002. ISSN 0034-6861. S2CID 85517132.
  10. ^ "NetKet — netket v3.0 documentation". www.netket.org. Retrieved 2021-04-09.
  11. ^ Schwendener, Thomas (2020-05-14). "Das Kommen und Gehen von IT-Profs an den ETHs". Inside IT. Retrieved 2021-05-17.
  12. ^ Carleo, Giuseppe; Troyer, Matthias (2017-02-10). "Solving the quantum many-body problem with artificial neural networks". Science. 355 (6325): 602–606. arXiv:1606.02318. Bibcode:2017Sci...355..602C. doi:10.1126/science.aag2302. ISSN 0036-8075. PMID 28183973. S2CID 206651104.
  13. ^ Torlai, Giacomo; Mazzola, Guglielmo; Carrasquilla, Juan; Troyer, Matthias; Melko, Roger; Carleo, Giuseppe (2018-05-01). "Neural-network quantum state tomography". Nature Physics. 14 (5): 447–450. arXiv:1703.05334. Bibcode:2018NatPh..14..447T. doi:10.1038/s41567-018-0048-5. ISSN 1745-2481. S2CID 125415859. Retrieved 2018-11-14.
  14. ^ Torlai, Giacomo; Timar, Brian; van Nieuwenburg, Evert P. L.; Levine, Harry; Omran, Ahmed; Keesling, Alexander; Bernien, Hannes; Greiner, Markus; Vuletić, Vladan; Lukin, Mikhail D.; Melko, Roger G.; Endres, Manuel (2019-12-06). "Integrating Neural Networks with a Quantum Simulator for State Reconstruction". Physical Review Letters. 123 (23): 230504. arXiv:1904.08441. Bibcode:2019PhRvL.123w0504T. doi:10.1103/PhysRevLett.123.230504. hdl:1721.1/136583. PMID 31868463. S2CID 120417032. Retrieved 2021-04-09.
  15. ^ Carleo, Giuseppe; Becca, Federico; Schiro, Marco; Fabrizio, Michele (2012-02-06). "Localization and Glassy Dynamics Of Many-Body Quantum Systems". Scientific Reports. 2: 243. arXiv:1109.2516. Bibcode:2012NatSR...2E.243C. doi:10.1038/srep00243. PMC 3272662. PMID 22355756. S2CID 17367662.
  16. ^ Schmitt, Markus; Heyl, Markus (2020-09-02). "Quantum Many-Body Dynamics in Two Dimensions with Artificial Neural Networks". Physical Review Letters. 125 (10): 100503. arXiv:1912.08828. Bibcode:2020PhRvL.125j0503S. doi:10.1103/PhysRevLett.125.100503. PMID 32955321. S2CID 209414859. Retrieved 2021-04-09.
  17. ^ Blaß, Benjamin; Rieger, Heiko (2016-12-01). "Test of quantum thermalization in the two-dimensional transverse-field Ising model". Scientific Reports. 6 (1): 38185. arXiv:1605.06258. Bibcode:2016NatSR...638185B. doi:10.1038/srep38185. ISSN 2045-2322. PMC 5131304. PMID 27905523.
  18. ^ Stokes, James; Izaac, Josh; Killoran, Nathan; Carleo, Giuseppe (2020-05-25). "Quantum Natural Gradient". Quantum. 4: 269. arXiv:1909.02108. Bibcode:2020Quant...4..269S. doi:10.22331/q-2020-05-25-269. S2CID 202537631. Retrieved 2020-06-29.
  19. ^ Ouellette, Jennifer. "AI learns to solve quantum state of many particles at once". New Scientist. Retrieved 2021-04-09.
  20. ^ Timmer, John (2017-02-10). "Neural network trained to solve quantum mechanical problems". Ars Technica. Retrieved 2021-04-09.
  21. ^ "A machine-learning revolution". Physics World. 2019-03-04. Retrieved 2021-04-09.
  22. ^ Andy Extance2020-04-21T08:30:00+01:00. "Quantum chemistry simulations offers beguiling possibility of 'solving chemistry'". Chemistry World. Retrieved 2021-04-09.{{cite web}}: CS1 maint: numeric names: authors list (link)
  23. ^ "Intelligent Machines are Teaching Themselves Quantum Physics". www.vice.com. 13 February 2017. Retrieved 2021-04-09.
  24. ^ Carleo, Giuseppe (2018). Neural-network quantum states. Institut des Hautes Études Scientifiques (IHÉS). doi:10.5446/46751.
  25. ^ Giuseppe Carleo: "Generative and variational modeling for quantum many-body physics", 9 October 2019, retrieved 2021-05-17
  26. ^ Williams, Jonathan. "Fellows". European Lab for Learning & Intelligent Systems. Retrieved 2021-04-09.
  27. ^ "Editorial Board - Machine Learning: Science and Technology - IOPscience". iopscience.iop.org. Retrieved 2021-04-09.
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