← all papers · overview

Quantum neural computation of entanglement is robust to noise and decoherence

Abstract

In previous work, we have proposed an entanglement indicator for a general multiqubit state, which can be "learned" by a quantum system, acting as a neural network. The indicator can be used for a pure or a mixed state, and it need not be "close" to any particular state; moreover, as the size of the system grows, the amount of additional training necessary diminishes. Here, we show that the indicator is stable to noise and decoherence.

Related papers

Ranked by semantic similarity — how closely each paper's abstract matches this one (100% = near-identical topic).