Observation of disorder-free localization and efficient disorder averaging on a quantum processor

One of the most challenging problems in the computational study of localization in quantum many-body systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. In this talk, I will present the results from our preprint arXiv:2410.06557, where we implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of dynamics and the initial states are entirely translationally invariant. The disorder strength and density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.