Catalysis of quantum tunneling by ancillary system learning

TL;DR

This paper introduces a machine learning-based method to enhance quantum tunneling probabilities by coupling a system with an ancillary system, optimizing parameters to maximize tunneling even under noise and decoherence.

Contribution

It presents a novel approach using ancillary systems and machine learning to improve quantum tunneling efficiency under fixed resource constraints.

Findings

Tunneling probability can be significantly increased with optimized ancillary coupling.

The method is robust against noise and decoherence effects.

Applicable to multi-particle and arbitrary coupling scenarios.

Abstract

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.