Fast generation of entanglement between coupled spins using optimization and deep learning methods

TL;DR

This paper demonstrates that machine learning and optimization techniques can efficiently generate maximally entangled states in coupled spin-1/2 systems in minimal time by optimizing control parameters, outperforming traditional methods.

Contribution

It introduces a novel approach using machine learning and optimization to maximize entanglement in coupled spins without targeting specific states, reducing control time.

Findings

Optimized controls increase entanglement faster than traditional methods.

Higher control bounds lead to quicker entanglement generation.

The approach is flexible and scalable to larger spin systems.

Abstract

Coupled spins form composite quantum systems which play an important role in many quantum technology applications, with an essential task often being the efficient generation of entanglement between two constituent qubits. The simplest such system is a pair of spins-$1/2$ coupled with Ising interaction, and in previous works various quantum control methods such as adiabatic processes, shortcuts to adiabaticity and optimal control have been employed to quickly generate there one of the maximally entangled Bell states. In this study, we use machine learning and optimization methods to produce maximally entangled states in minimum time, with the Rabi frequency and the detuning used as bounded control functions. We do not target a specific maximally entangled state, like the preceding studies, but rather find the controls which maximize the concurrence, leading thus automatically the system to the closest such state in shorter time. By increasing the bounds of the control functions we observe that the corresponding optimally selected maximally entangled state also changes and the necessary time to reach it is reduced. The present work demonstrates also that machine learning and optimization offer efficient and flexible techniques for the fast generation of entanglement in coupled spin systems, and we plan to extent it to systems involving more spins, for example spin chains.