Giuseppe Carleo Explained

Giuseppe Carleo
Citizenship:Italian
Known For:Neural network quantum states
Time-dependent variational Monte Carlo
Website:https://www.epfl.ch/labs/cqsl/
Education:Physics
Alma Mater:Sapienza University of Rome
International School for Advanced Studies
Thesis Title:Spectral and dynamical properties of strongly correlated systems
Thesis Url:http://hdl.handle.net/20.500.11767/4289
Thesis Year:2011
Doctoral Advisor:Stefano Baroni
Academic Advisors:Matthias Troyer
Discipline:Physics
Sub Discipline:Computational physics
Workplaces:EPFL (École Polytechnique Fédérale de Lausanne)
Main Interests:Machine learning
Quantum computing
Condensed matter physics

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

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.[11]

Research

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]

Distinctions

He is a scholar at the ELLIS Society (since 2020)[25] and a member of the editorial board of Machine Learning Science and Technology (since 2019).[26]

Selected works

External links

Notes and References

  1. Web site: People. 2021-04-09. www.epfl.ch. en-US.
  2. Web site: 11 new professors appointed at the two Federal Institutes of Technology ETH-Board. 2021-04-09. www.ethrat.ch. 2021-08-18. https://web.archive.org/web/20210818201338/https://www.ethrat.ch/en/media/releases/appointments-may20. dead.
  3. Web site: Spectral and dynamical properties of strongly correlated systems. 2021-04-09. iris.sissa.it.
  4. Web site: Quantum Dynamics of Strongly Correlated Systems and Ultra-Cold Atomic Gases MASCARA Project. 2021-05-12.
  5. Web site: Course Catalogue - ETH Zurich. 2021-05-12.
  6. Web site: Maschinelles Lernen: Neuronale Netze als Quantensimulator. 2021-05-17. www.spektrum.de. de.
  7. Web site: Giuseppe Carleo. Simons Foundation. 2021-04-21. 2018-02-05.
  8. Siegfried. Tom. 2020-08-27. Why some artificial intelligence is smart until it's dumb. Knowable Magazine . en. 10.1146/knowable-082720-1. 225302152 . free.
  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. en. 91. 4. 045002. 10.1103/RevModPhys.91.045002. 1903.10563. 2019RvMP...91d5002C. 85517132. 0034-6861.
  10. Web site: NetKet — netket v3.0 documentation. 2021-04-09. www.netket.org.
  11. Web site: Schwendener. Thomas. 2020-05-14. Das Kommen und Gehen von IT-Profs an den ETHs. 2021-05-17. Inside IT.
  12. Carleo. Giuseppe. Troyer. Matthias. 2017-02-10. Solving the quantum many-body problem with artificial neural networks. Science. en. 355. 6325. 602–606. 10.1126/science.aag2302. 28183973. 1606.02318. 2017Sci...355..602C. 206651104. 0036-8075.
  13. 10.1038/s41567-018-0048-5. 1745-2481. 14. 5. 447–450. Torlai. Giacomo. Mazzola. Guglielmo. Carrasquilla. Juan. Troyer. Matthias. Melko. Roger. Carleo. Giuseppe. Neural-network quantum state tomography. Nature Physics. 2018-11-14. 2018-05-01. 1703.05334. 2018NatPh..14..447T. 125415859.
  14. 10.1103/PhysRevLett.123.230504. 123. 23. 230504. 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. Integrating Neural Networks with a Quantum Simulator for State Reconstruction. Physical Review Letters. 2021-04-09. 2019-12-06. 31868463. 1904.08441. 2019PhRvL.123w0504T. 1721.1/136583. 120417032.
  15. 10.1038/srep00243. 2. 243. Carleo. Giuseppe. Becca. Federico. Schiro. Marco. Fabrizio. Michele. Localization and Glassy Dynamics Of Many-Body Quantum Systems. Scientific Reports. 2012-02-06. 22355756. 3272662. 1109.2516. 2012NatSR...2E.243C. 17367662.
  16. 10.1103/PhysRevLett.125.100503. 125. 10. 100503. Schmitt. Markus. Heyl. Markus. Quantum Many-Body Dynamics in Two Dimensions with Artificial Neural Networks. Physical Review Letters. 2021-04-09. 2020-09-02. 32955321. 1912.08828. 2020PhRvL.125j0503S. 209414859.
  17. 10.1038/srep38185. 2045-2322. 6. 1. 38185. Blaß. Benjamin. Rieger. Heiko. Test of quantum thermalization in the two-dimensional transverse-field Ising model. Scientific Reports. 2016-12-01. 27905523. 5131304. 1605.06258. 2016NatSR...638185B.
  18. 10.22331/q-2020-05-25-269 . 1909.02108. 4. 269. Stokes. James. Izaac. Josh. Killoran. Nathan. Carleo. Giuseppe. Quantum Natural Gradient. Quantum. 2020-06-29. 2020-05-25. 2020Quant...4..269S . 202537631.
  19. Web site: Ouellette. Jennifer. AI learns to solve quantum state of many particles at once. 2021-04-09. New Scientist. en-US.
  20. Web site: Timmer. John. 2017-02-10. Neural network trained to solve quantum mechanical problems. 2021-04-09. Ars Technica. en-us.
  21. Web site: 2019-03-04. A machine-learning revolution. 2021-04-09. Physics World. en-GB.
  22. Web site: Andy Extance2020-04-21T08:30:00+01:00. Quantum chemistry simulations offers beguiling possibility of 'solving chemistry'. 2021-04-09. Chemistry World. en.
  23. Web site: Intelligent Machines are Teaching Themselves Quantum Physics. 2021-04-09. www.vice.com. 13 February 2017 . en.
  24. Institut des Hautes Études Scientifiques (IHÉS). Carleo, Giuseppe. Neural-network quantum states. 2018 . 10.5446/46751.
  25. Web site: Williams. Jonathan. Fellows. 2021-04-09. European Lab for Learning & Intelligent Systems. en.
  26. Web site: Editorial Board - Machine Learning: Science and Technology - IOPscience. 2021-04-09. iopscience.iop.org.

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