Disentangling quantum autoencoder

TL;DR

The paper introduces the disentangling quantum autoencoder (DQAE), which efficiently encodes entangled states into single-qubit states, improving quantum communication and storage, with effective training methods for various state classes.

Contribution

It proposes the DQAE for disentangling entangled states into product states, demonstrating exponential efficiency gains and low training data requirements for specific state classes.

Findings

DQAE achieves exponential improvement in transporting entangled states.

Training can be unsupervised using variational algorithms or Metropolis methods.

Low training data suffices for stabilizer states and certain evolved states.

Abstract

Entangled quantum states are highly sensitive to noise, which makes it difficult to transfer them over noisy quantum channels or to store them in quantum memory. Here, we propose the disentangling quantum autoencoder (DQAE) to encode entangled states into single-qubit product states. The DQAE provides an exponential improvement in the number of copies needed to transport entangled states across qubit-loss or leakage channels compared to unencoded states. The DQAE can be trained in an unsupervised manner from entangled quantum data. For general states, we train via variational quantum algorithms based on gradient descent with purity-based cost functions, while stabilizer states can be trained via a Metropolis algorithm. For particular classes of states, the number of training data needed to generalize is surprisingly low: For stabilizer states, DQAE generalizes by learning from a number of training data that scales linearly with the number of qubits, while only $1$ training sample is sufficient for states evolved with the transverse-field Ising Hamiltonian. Our work provides practical applications for enhancing near-term quantum computers.