A Quantum Neural Network Computes Entanglement

TL;DR

This paper demonstrates that a quantum neural network can be trained to compute quantum entanglement, solving a longstanding problem in quantum computing where no closed-form algorithm exists.

Contribution

It introduces a quantum neural network model capable of calculating entanglement, outperforming classical neural nets and algorithmic quantum computers in this task.

Findings

Quantum neural networks can be trained to compute entanglement.

Simulation results show successful entanglement calculation.

The method outperforms classical neural nets and existing algorithms.

Abstract

An outstanding problem in quantum computing is the calculation of entanglement, for which no closed-form algorithm exists. Here we solve that problem, and demonstrate the utility of a quantum neural computer, by showing, in simulation, that such a device can be trained to calculate the entanglement of an input state, something neither an algorithmic quantum computer nor a classical neural net can do.