arXiv subjects

Renzo Testa

Publications and source records attributed to Renzo Testa.

3 recordsLinked to original sources

arXiv2026

Generating persistent-current superpositions in Bose-Einstein condensates using dynamic optical potentials

Precise and flexible manipulation of the motional state of ultracold atoms is a fundamental enabling technology for diverse applications such as quantum sensing and quantum computation. In this paper we propose a general, simple and highly efficient method to engineer the motional state of a Bose-Einstein condensate with time-dependent optical fields, which can be realized experimentally with existing light sculpting techniques. We demonstrate numerically how to engineer superpositions of persistent currents in a toroidal trap, achieving very high fidelity. We also study in detail the stability of the state over time, and we present an analytical two-state model that approximates well the evolution of the state in presence of self-interactions.

cond-mat.quant-gas↗

arXiv2023

Machine Learning Catalysis of Quantum Tunneling

Optimizing the probability of quantum tunneling between two states, while keeping the resources of the underlying physical system constant, is a task of key importance due to its critical role in various applications. We show that, by applying Machine Learning techniques when the system is coupled to an ancilla, one optimizes the parameters of both the ancillary component and the coupling, ultimately resulting in the maximization of the tunneling probability. We provide illustrative examples for the paradigmatic scenario involving a two-mode system and a two-mode ancilla in the presence of several interacting particles. Physically, the increase of the tunneling probability is rooted in the decrease of the two-well asymmetry due to the coherent oscillations induced by the coupling to the ancilla. We also argue that the enhancement of the tunneling probability is not hampered by weak coupling to noisy environments.

quant-ph↗

arXiv2023

Catalysis of quantum tunneling by ancillary system learning

Given the key role that quantum tunneling plays in a wide range of applications, a crucial objective is to maximize the probability of tunneling from one quantum state/level to another, while keeping the resources of the underlying physical system fixed. In this work, we demonstrate that an effective solution to this challenge can be achieved by coupling the tunneling system with an ancillary system of the same kind. By utilizing machine learning techniques, the parameters of both the ancillary system and the coupling can be optimized, leading to the maximization of the tunneling probability. We provide illustrative examples for the paradigmatic scenario involving a two-mode system and a two-mode ancilla with arbitrary couplings and in the presence of several interacting particles. Importantly, the enhancement of the tunneling probability appears to be minimally affected by noise and decoherence in both the system and the ancilla.

quant-ph↗