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.
Quantum chemistry with neural-network wave functions
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
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
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.