Giuseppe Carleo

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.{{Cite web|title=Spectral and dynamical properties of strongly correlated systems|url=https://iris.sissa.it/handle/20.500.11767/4289|access-date=2021-04-09|website=iris.sissa.it}}

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.{{Cite web| title = Quantum Dynamics of Strongly Correlated Systems and Ultra-Cold Atomic Gases {{!}} MASCARA Project| accessdate = 2021-05-12| url = https://cordis.europa.eu/project/id/327143}} 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.{{Cite web| title = Course Catalogue - ETH Zurich| accessdate = 2021-05-12| url = http://www.vvz.ethz.ch/Vorlesungsverzeichnis/lerneinheit.view?lerneinheitId=113081&semkez=2017S&lang=en}}{{Cite web|title=Maschinelles Lernen: Neuronale Netze als Quantensimulator|url=https://www.spektrum.de/news/neuronale-netze-als-quantensimulator/1437914|access-date=2021-05-17|website=www.spektrum.de|language=de}} 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.{{Cite web| title = Giuseppe Carleo| work = Simons Foundation| accessdate = 2021-04-21| date = 2018-02-05| url = https://www.simonsfoundation.org/people/giuseppe-carleo/}} Here he became a member of a team of researchers developing numerical methods at the intersection of machine learning and quantum science.{{Cite journal|last=Siegfried|first=Tom|date=2020-08-27|title=Why some artificial intelligence is smart until it's dumb|url=https://knowablemagazine.org/article/technology/2020/why-some-artificial-intelligence-smart-until-its-dumb|journal=Knowable Magazine |language=en|doi=10.1146/knowable-082720-1|s2cid=225302152 |doi-access=free}}{{Cite journal|last1=Carleo|first1=Giuseppe|last2=Cirac|first2=Ignacio|last3=Cranmer|first3=Kyle|last4=Daudet|first4=Laurent|last5=Schuld|first5=Maria|last6=Tishby|first6=Naftali|last7=Vogt-Maranto|first7=Leslie|last8=Zdeborová|first8=Lenka|date=2019-12-06|title=Machine learning and the physical sciences|url=https://link.aps.org/doi/10.1103/RevModPhys.91.045002|journal=Reviews of Modern Physics|language=en|volume=91|issue=4|pages=045002|doi=10.1103/RevModPhys.91.045002|arxiv=1903.10563|bibcode=2019RvMP...91d5002C|s2cid=85517132|issn=0034-6861}} Since 2018 he has been leading the open-source project NetKet.{{Cite web|title=NetKet — netket v3.0 documentation|url=https://www.netket.org/|access-date=2021-04-09|website=www.netket.org}}

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.{{Cite web|last=Schwendener|first=Thomas|date=2020-05-14|title=Das Kommen und Gehen von IT-Profs an den ETHs|url=https://www.inside-it.ch/de/post/das-kommen-und-gehen-von-it-profs-an-den-eths-20200514|access-date=2021-05-17|website=Inside IT}}

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{{Cite journal|last1=Carleo|first1=Giuseppe|last2=Troyer|first2=Matthias|date=2017-02-10|title=Solving the quantum many-body problem with artificial neural networks|url=https://www.science.org/doi/10.1126/science.aag2302|journal=Science|language=en|volume=355|issue=6325|pages=602–606|doi=10.1126/science.aag2302|pmid=28183973|arxiv=1606.02318|bibcode=2017Sci...355..602C|s2cid=206651104|issn=0036-8075}} and constitutes one of the early applications of machine learning techniques in modern many-body quantum physics. An application of this representation{{Cite journal| doi = 10.1038/s41567-018-0048-5| issn = 1745-2481| volume = 14| issue = 5| pages = 447–450| last1 = Torlai| first1 = Giacomo| last2 = Mazzola| first2 = Guglielmo| last3 = Carrasquilla| first3 = Juan| last4 = Troyer| first4 = Matthias| last5 = Melko| first5 = Roger| last6 = Carleo| first6 = Giuseppe| title = Neural-network quantum state tomography| journal = Nature Physics| accessdate = 2018-11-14| date = 2018-05-01| arxiv = 1703.05334| bibcode = 2018NatPh..14..447T| s2cid = 125415859| url = https://www.nature.com/articles/s41567-018-0048-5}} is for example used for quantum tomography of interacting Rydberg atoms.{{Cite journal| doi = 10.1103/PhysRevLett.123.230504| volume = 123| issue = 23| pages = 230504| last1 = Torlai| first1 = Giacomo| last2 = Timar| first2 = Brian| last3 = van Nieuwenburg| first3 = Evert P. L.| last4 = Levine| first4 = Harry| last5 = Omran| first5 = Ahmed| last6 = Keesling| first6 = Alexander| last7 = Bernien| first7 = Hannes| last8 = Greiner| first8 = Markus| last9 = Vuletić| first9 = Vladan| last10 = Lukin| first10 = Mikhail D.| last11 = Melko| first11 = Roger G.| last12 = Endres| first12 = Manuel| title = Integrating Neural Networks with a Quantum Simulator for State Reconstruction| journal = Physical Review Letters| accessdate = 2021-04-09| date = 2019-12-06| pmid = 31868463| arxiv = 1904.08441| bibcode = 2019PhRvL.123w0504T| hdl = 1721.1/136583| s2cid = 120417032| url = https://link.aps.org/doi/10.1103/PhysRevLett.123.230504}}

In 2011, he also co-developed the time-dependent variational Monte Carlo method,{{Cite journal| doi = 10.1038/srep00243| volume = 2| pages = 243| last1 = Carleo| first1 = Giuseppe| last2 = Becca| first2 = Federico| last3 = Schiro| first3 = Marco| last4 = Fabrizio| first4 = Michele| title = Localization and Glassy Dynamics Of Many-Body Quantum Systems| journal = Scientific Reports| date = 2012-02-06| pmid = 22355756| pmc = 3272662| arxiv = 1109.2516| bibcode = 2012NatSR...2E.243C| s2cid = 17367662}} 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.{{Cite journal| doi = 10.1103/PhysRevLett.125.100503| volume = 125| issue = 10| pages = 100503| last1 = Schmitt| first1 = Markus| last2 = Heyl| first2 = Markus| title = Quantum Many-Body Dynamics in Two Dimensions with Artificial Neural Networks| journal = Physical Review Letters| accessdate = 2021-04-09| date = 2020-09-02| pmid = 32955321| arxiv = 1912.08828| bibcode = 2020PhRvL.125j0503S| s2cid = 209414859| url = https://link.aps.org/doi/10.1103/PhysRevLett.125.100503}}{{Cite journal| doi = 10.1038/srep38185| issn = 2045-2322| volume = 6| issue = 1| pages = 38185| last1 = Blaß| first1 = Benjamin| last2 = Rieger| first2 = Heiko| title = Test of quantum thermalization in the two-dimensional transverse-field Ising model| journal = Scientific Reports| date = 2016-12-01| pmid = 27905523| pmc = 5131304| arxiv = 1605.06258| bibcode = 2016NatSR...638185B}}

Carleo has also contributed to the development of quantum algorithms, especially in the context of variational quantum simulation.{{Cite journal| doi = 10.22331/q-2020-05-25-269 |arxiv=1909.02108| volume = 4| pages = 269| last1 = Stokes| first1 = James| last2 = Izaac| first2 = Josh| last3 = Killoran| first3 = Nathan| last4 = Carleo| first4 = Giuseppe| title = Quantum Natural Gradient| journal = Quantum| accessdate = 2020-06-29| date = 2020-05-25|bibcode=2020Quant...4..269S | s2cid = 202537631| url = https://quantum-journal.org/papers/q-2020-05-25-269/}}

His research has been featured in news outlets such as New Scientist,{{Cite web|last=Ouellette|first=Jennifer|title=AI learns to solve quantum state of many particles at once|url=https://www.newscientist.com/article/2120856-ai-learns-to-solve-quantum-state-of-many-particles-at-once/|access-date=2021-04-09|website=New Scientist|language=en-US}} Ars Technica,{{Cite web|last=Timmer|first=John|date=2017-02-10|title=Neural network trained to solve quantum mechanical problems|url=https://arstechnica.com/science/2017/02/neural-network-trained-to-solve-quantum-mechanical-problems/|access-date=2021-04-09|website=Ars Technica|language=en-us}} Physics World,{{Cite web|date=2019-03-04|title=A machine-learning revolution|url=https://physicsworld.com/a/a-machine-learning-revolution/|access-date=2021-04-09|website=Physics World|language=en-GB}} Chemistry World,{{Cite web|last=Andy Extance2020-04-21T08:30:00+01:00|title=Quantum chemistry simulations offers beguiling possibility of 'solving chemistry'|url=https://www.chemistryworld.com/news/quantum-chemistry-simulations-offers-beguiling-possibility-of-solving-chemistry/4011541.article|access-date=2021-04-09|website=Chemistry World|language=en}} and Vice.{{Cite web|title=Intelligent Machines are Teaching Themselves Quantum Physics|url=https://www.vice.com/en/article/machine-learning-quantum-physics-perimeter-institute-roger-melko/|access-date=2021-04-09|website=Vice.com|date=13 February 2017 |language=en}} Some of his lectures are also available on YouTube.{{Cite AV media| publisher = Institut des Hautes Études Scientifiques (IHÉS)| people = Carleo, Giuseppe| title = Neural-network quantum states| date = 2018 | doi = 10.5446/46751}}{{Citation|title=Giuseppe Carleo: "Generative and variational modeling for quantum many-body physics"| date=9 October 2019 |url=https://www.youtube.com/watch?v=dTmh7t0rFwk|language=en|access-date=2021-05-17}}

He is a scholar at the ELLIS Society (since 2020){{Cite web|last=Williams|first=Jonathan|title=Fellows|url=http://ellis.eu/fellows|access-date=2021-04-09|website=European Lab for Learning & Intelligent Systems|language=en}} and a member of the editorial board of Machine Learning Science and Technology (since 2019).{{Cite web|title=Editorial Board - Machine Learning: Science and Technology - IOPscience|url=https://iopscience.iop.org/journal/2632-2153/page/editorial-board|access-date=2021-04-09|website=iopscience.iop.org}}

• {{Cite journal|last1=Carleo|first1=Giuseppe|last2=Troyer|first2=Matthias|date=2017-02-10|title=Solving the quantum many-body problem with artificial neural networks|url=https://www.science.org/doi/10.1126/science.aag2302|journal=Science|language=en|volume=355|issue=6325|pages=602–606|doi=10.1126/science.aag2302|pmid=28183973|arxiv=1606.02318|bibcode=2017Sci...355..602C|s2cid=206651104|issn=0036-8075}}
• {{Cite journal|last1=Torlai|first1=Giacomo|last2=Mazzola|first2=Guglielmo|last3=Carrasquilla|first3=Juan|last4=Troyer|first4=Matthias|last5=Melko|first5=Roger|last6=Carleo|first6=Giuseppe|date=May 2018|title=Neural-network quantum state tomography|url=http://www.nature.com/articles/s41567-018-0048-5|journal=Nature Physics|language=en|volume=14|issue=5|pages=447–450|doi=10.1038/s41567-018-0048-5|arxiv=1703.05334|bibcode=2018NatPh..14..447T|s2cid=125415859|issn=1745-2473}}
• {{Cite journal|last1=Carleo|first1=Giuseppe|last2=Cirac|first2=Ignacio|last3=Cranmer|first3=Kyle|last4=Daudet|first4=Laurent|last5=Schuld|first5=Maria|last6=Tishby|first6=Naftali|last7=Vogt-Maranto|first7=Leslie|last8=Zdeborová|first8=Lenka|date=2019-12-06|title=Machine learning and the physical sciences|url=https://link.aps.org/doi/10.1103/RevModPhys.91.045002|journal=Reviews of Modern Physics|language=en|volume=91|issue=4|pages=045002|doi=10.1103/RevModPhys.91.045002|arxiv=1903.10563|bibcode=2019RvMP...91d5002C|s2cid=85517132|issn=0034-6861}}
• {{Cite journal|last1=Carleo|first1=Giuseppe|last2=Becca|first2=Federico|last3=Schiró|first3=Marco|last4=Fabrizio|first4=Michele|date=2012-02-06|title=Localization and Glassy Dynamics Of Many-Body Quantum Systems|journal=Scientific Reports|language=en|volume=2|issue=1|pages=243|doi=10.1038/srep00243|issn=2045-2322|pmc=3272662|pmid=22355756|arxiv=1109.2516|bibcode=2012NatSR...2E.243C}}
• {{Cite journal|last1=Stokes|first1=James|last2=Izaac|first2=Josh|last3=Killoran|first3=Nathan|last4=Carleo|first4=Giuseppe|date=2020-05-25|title=Quantum Natural Gradient|url=https://quantum-journal.org/papers/q-2020-05-25-269/|journal=Quantum|language=en|volume=4|pages=269|doi=10.22331/q-2020-05-25-269 |arxiv=1909.02108|bibcode=2020Quant...4..269S |s2cid=202537631|issn=2521-327X}}
• {{Cite journal| doi = 10.1103/PhysRevLett.122.250502| volume = 122| issue = 25| pages = 250502| last1 = Hartmann| first1 = Michael J.| last2 = Carleo| first2 = Giuseppe| title = Neural-Network Approach to Dissipative Quantum Many-Body Dynamics| journal = Physical Review Letters| date = 2019-06-28| pmid = 31347862| arxiv = 1902.05131| bibcode = 2019PhRvL.122y0502H| s2cid = 119357494| url = https://link.aps.org/doi/10.1103/PhysRevLett.122.250502}}
• {{Cite journal| doi = 10.1038/s41467-018-07520-3| issn = 2041-1723| volume = 9| issue = 1| pages = 5322| last1 = Carleo| first1 = Giuseppe| last2 = Nomura| first2 = Yusuke| last3 = Imada| first3 = Masatoshi| title = Constructing exact representations of quantum many-body systems with deep neural networks| journal = Nature Communications| date = 2018-12-14| pmid = 30552316| pmc = 6294148| arxiv = 1802.09558| bibcode = 2018NatCo...9.5322C| s2cid = 54631964}}
• {{Cite journal| doi = 10.1038/s41567-019-0545-1| issn = 1745-2481| volume = 15| issue = 9| pages = 887–892| last1 = Melko| first1 = Roger G.| last2 = Carleo| first2 = Giuseppe| last3 = Carrasquilla| first3 = Juan| last4 = Cirac| first4 = J. Ignacio| title = Restricted Boltzmann machines in quantum physics| journal = Nature Physics| date = 2019-09-01| bibcode = 2019NatPh..15..887M| s2cid = 195367448}}
• {{Cite journal| doi = 10.1038/s41467-020-15724-9| issn = 2041-1723| volume = 11| issue = 1| pages = 2368| last1 = Choo| first1 = Kenny| last2 = Mezzacapo| first2 = Antonio| last3 = Carleo| first3 = Giuseppe| title = Fermionic neural-network states for ab-initio electronic structure| journal = Nature Communications| date = 2020-05-12| pmid = 32398658| pmc = 7217823| arxiv = 1909.12852| bibcode = 2020NatCo..11.2368C}}

{{Reflist}}

• {{Google Scholar id|id=e9I38PEAAAAJ}}
• Website of the [https://www.epfl.ch/labs/cqsl/ Laboratory of Computational Quantum Science]

{{Authority control}}

{{DEFAULTSORT:Carleo, Giuseppe}}

Category:1984 births

Category:Living people

Category:Sapienza University of Rome alumni

Category:ETH Zurich alumni

Category:Academic staff of the École Polytechnique Fédérale de Lausanne

Category:Italian physicists