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.
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.
Simulating quantum computing with classical machine learning
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.