Creating and concentrating quantum resource states in noisy environments using a quantum neural network

TL;DR

This paper introduces a versatile quantum state preparation method using a driven quantum network with random fermionic nodes, capable of creating high-fidelity entangled states even in noisy environments, and includes entanglement concentration techniques.

Contribution

A unified, robust quantum state preparation scheme utilizing a driven quantum network with training of weights and phases, effective under noise and capable of entanglement concentration.

Findings

Method creates high-fidelity entangled states in noisy conditions.

Training parameters compensates for energy decay and dephasing.

Entanglement concentration achieved in highly noisy systems.

Abstract

Quantum information processing tasks require exotic quantum states as a prerequisite. They are usually prepared with many different methods tailored to the specific resource state. Here we provide a versatile unified state preparation scheme based on a driven quantum network composed of randomly-coupled fermionic nodes. The output of such a system is then superposed with the help of linear mixing where weights and phases are trained in order to obtain desired output quantum states. We explicitly show that our method is robust and can be utilized to create almost perfect maximally entangled, NOON, W, cluster, and discorded states. Furthermore, the treatment includes energy decay in the system as well as dephasing and depolarization. Under these noisy conditions we show that the target states are achieved with high fidelity by tuning controllable parameters and providing sufficient strength to the driving of the quantum network. Finally, in very noisy systems, where noise is comparable to the driving strength, we show how to concentrate entanglement by mixing more states in a larger network.