Disentangling quantum autoencoder
File(s)
Accepted version
Author(s)
Sireesh, Adithya
Alhajri, Abdulla
Kim, MS
Haug, Tobias
Type
Journal Article
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.
Date Issued
2025-12-01
Date Acceptance
2025-08-15
Citation
Quantum Science and Technology, 2025, 10 (4)
ISSN
2058-9565
Publisher
IOP Publishing
Journal / Book Title
Quantum Science and Technology
Volume
10
Issue
4
Copyright Statement
© 2025 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Subjects
Physical Sciences
Physics
Physics, Multidisciplinary
quantum autoencoder
quantum communication
quantum information
quantum machine learning
Quantum Science & Technology
Science & Technology
Publication Status
Published
Article Number
045023
Date Publish Online
2025-08-27