News

New benchmark helps solve the hardest quantum problems

Published:18.10.24 — Predicting the behavior of many interacting quantum particles is a complicated process but is key to harness quantum computing for real-world applications. A collaboration of researchers led by EPFL has developed a method for comparing quantum algorithms and identifying which quantum problems are the hardest to solve.

EPFL professor Giuseppe Carleo cited in Nobel Prize announcement

Published:09.10.24 — The 2024 Nobel Prize in Physics has been awarded to John J. Hopfield and Geoffrey E. Hinton for the invention of artificial neural networks which lie at the heart of machine learning and artificial intelligence. Among the many groundbreaking applications of this work, the Nobel Committee highlighted the pioneering work by QSE Center researcher Giuseppe Carleo in their scientific backgrounder.

Riccardo Rossi wins Hermann Kümmel Early Achievement Award

Published:27.08.24 — EPFL researcher Riccardo Rossi has been recognized for his groundbreaking work in computational quantum field theory, earning the prestigious Hermann Kümmel Early Achievement Award in Many-Body Physics.

Quantum chemistry with neural-network wave functions

Published:10.08.23 — A recent review article in Nature Reviews Chemistry presents a comprehensive analysis of the integration of neural-network wave functions within quantum chemistry. Titled "Ab initio quantum chemistry with neural-network wave functions," this review offers insights into the potential and challenges of using machine learning techniques to solve the fundamental equations of quantum mechanics.

CQSL at the APS March Meeting in Las Vegas

Published:03.03.23 — CQSL is presenting several of its works at the APS March Meeting in Las Vegas, between March 6th and Friday 10th

The expressive power of neural networks in quantum physics

Published:28.11.22 — A collaboration between the CQSL lab at EPFL and the Hebrew University of Jerusalem in Israel has studied the expressive power of neural network representation of quantum states. By means of an efficient mapping of tensor contractions to deep neural networks, the work establishes for the first time the efficient representability of one-dimensional gapped ground states by means of neural quantum states. Other connections to tensor-network states are also discussed. 

'Ghost' electrons used to reconstruct behaviour of quantum systems

Published:04.08.22 — Physicists at EPFL and the Flatiron Institute’s Center for Computational Quantum Physics have created a new way to simulate quantum entanglement between interacting particles. Their approach involves adding extra, fictitious particles controlled by an artificial intelligence technique called a neural network.

Four SB researchers awarded ERC Consolidator Grants

Published:17.03.22 — Four professors at EPFL’s School of Basic Sciences have been awarded Consolidator Grants from the European Research Council (ERC). As Switzerland currently has a non-association status to Horizon Europe, their projects will be financed by Switzerland’s State Secretariat for Education, Research and Innovation (SERI).

CQSL research featured at the APS "March Meeting" in Chicago

Published:11.03.22 — The incoming March meeting of the American Physical Society will feature several presentations of the research performed at EPFL's Computational Quantum Science Lab (CQSL) and in collaboration with other institutions worldwide. CQSL work will be presented in about 8 oral presentations, including 2 invited talks. 

Machine-Learning Augmented Sampling for the Molecular Sciences

Published:21.02.22 — CQSL is a proud sponsor of the forthcoming CECAM workshop entitled "Machine Learning Augmented Sampling for the Molecular Sciences".

Quantum Magnetism on the Pyrochlore Lattice

Published:01.11.21 — A collaboration between CQSL and the University of Zurich has studied the elusive properties of quantum magnets on the Pyrochlore Lattice. The work, now published in Physical Review X, is based on extensive numerical simulations relying on novel ways to represent complex quantum wave functions, including deep convolutional neural networks.

Running quantum software on a classical computer

Published:03.08.21 — Two physicists, from EPFL and Columbia University, have introduced an approach for simulating the quantum approximate optimization algorithm using a traditional computer. Instead of running the algorithm on advanced quantum processors, the new approach uses a classical machine-learning algorithm that closely mimics the behavior of near-term quantum computers.

Neural-Network Quantum States for Nuclear Matter

Published:07.07.21 — The first application of neural-network quantum states to nuclear matter has been published in Physical Review Letters, in a collaboration between CQSL and Argonne National Laboratory, in Chicago. 

Simulating quantum computing with classical machine learning

Published:05.07.21 — A recent CQSL work published in NPJ Quantum Information shows how machine learning techniques can be used to simulate the inner workings of near-term quantum computers.

CQSL at the APS March Meeting 2021

Published:16.03.21 — The Computational Quantum Science Lab will be present at the virtual edition of the 2021 APS March Meeting, with about 8 contributed talks and 2 invited seminars.