Statistical physics of feedback-based driven colloidal suspensions

When is it useful to stop a search process in its track, just to start all over again from the beginning? It turns out, that in very noisy processes in which the fluctuations in the time to reach a target are larger than its mean; this is often the case. A striking example of this phenomenon, is the fact that in some situations an enzyme should unbind its substrate to hasten a catalytic reaction. Other manifestations of expedited processes due to renewal and resetting appear naturally in many circumstances, such as in foraging animals, computer algorithms, and queuing theory. Diffusion with stochastic resetting serves as a paradigmatic model to study these phenomena, but the lack of a well-controlled platform by which this process can be studied experimentally has been a major impediment to research in the field. Here, we report the experimental realization of colloidal particle diffusion and resetting via holographic optical tweezers. We provide the first experimental corroboration of central theoretical results and go on to measure the energetic cost of resetting in steady-state and first-passage scenarios. In both cases, we show that this cost cannot be made arbitrarily small because of fundamental constraints on realistic resetting protocols.