The Computational Quantum Science Lab researches, develops, and promotes a broad range of advanced computational techniques to study quantum phenomena. Under the direction of Giuseppe Carleo, the methods developed at CQSL include innovative machine learning techniques to study Condensed Matter, Ultracold Atoms, Electronic Structure, as well as to characterize Quantum Devices. In addition to numerical approaches based on classical computers, novel algorithms to simulate quantum processes and suitable for near-term quantum devices are also being developed.
Running quantum software on a classical computer
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
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.