Speaker
Description
Simulating the low-temperature properties of frustrated quantum Ising models is a paradigmatic problem in condensed matter physics. It has recently gained strong interest in the context of quantum-enhanced optimization performed via quantum annealers and of quantum simulation in Rydberg-atom experiments.
We use a recently-developed self-learning projection quantum Monte Carlo algorithm driven by neural-network states to simulate both short-range and long-range disordered quantum Ising models at zero-temperature.
Our results show that, if the neural ansatz is big enough, this technique provides unbiased estimates of ground-state properties, accessing regimes not easily accessible so far.
In particular, we investigate the spin-glass phase of the 2D quantum Edwards-Anderson model and analyze the quantum critical point. Furthermore, we obtain results consistent with replica symmetry breaking.
Lastly, we study the properties of geometrically-frustrated quantum magnets with either nearest-neighbour or power-law type interactions, relevant to describe Rydberg atoms in optical tweezers. Our preliminary results confirm the existence of the so called “order-by-disorder” phenomenon in which the ordered clock-phase arises from quantum fluctuations. Future findings in these systems could be relevant for comparison with experiments on quantum simulators based on trapped atoms, where the interaction is highly controllable.
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