I like to develop theory and simulations for condensed matter and statistical physics models. In particular, I aim to combine established methods, such as electronic solvers and (quantum) Monte Carlo, with new ones such as machine learning and quantum computing.
Over the years, my main research interest has been the physics of compressed hydrogen. Hydrogen is the simplest and most abundant element in the Universe, yet its phase diagram is poorly understood. Dense hydrogen undergoes a number of exotic phase transitions that defy expectations; these are important for technological applications and planetary science. Beside that hydrogen is an ideal playground to develop new theories and simulations.
I am also interested in understanding the real impact of machine learning methods and quantum computing in solving relevant problems in physics and technology. To this end, I develop quantum algorithms for chemistry, materials science, and sampling, and perform rigorous assessments to understand if and when "quantum advantage" will be expected in these fields.
Selected and recent publications
G. Mazzola & G. Carleo, Exponential challenges in unbiasing quantum Monte Carlo algorithms with quantum computers, arXiv: 2205.09203 (2022)
D Layden, G Mazzola et. al., Quantum-enhanced Markov chain Monte Carlo, arXiv:2203.12497 (2022)
G. Mazzola, Sampling, rates, and reaction currents through reverse stochastic quantization on quantum computers, Phys. Rev. A, 104, 022431 (2021)
B Cheng, G Mazzola et. al, Evidence for supercritical behaviour of high-pressure liquid hydrogen, Nature 585 (7824), 217-220 (2020)
G. Torlai, G. Mazzola et. al., Neural-network quantum state tomography, Nature Physics, 14, 447–450 (2018)
G. Mazzola, R. Helled & S. Sorella, Phase diagram of hydrogen and a hydrogen-helium mixture at planetary conditions by quantum Monte Carlo simulations, Phys. Rev. Lett, 120, (2), 025701 (2018).