Evidence map›Paper›PMID 42187978›Full record

ArticleEntropy (Basel, Switzerland)2026

Autoencoding-Assisted Quantum Cloning Machine.

Qian Jun Beh, Moritz Straeter, Zeen Sun, Leong Chuan Kwek, Yuancheng Zhan

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Qian Jun BehCentre for Quantum Technologies (CQT), National University of Singapore, Singapore 117543, Singapore.ORCID 0009-0002-1312-4312
Moritz StraeterCentre for Quantum Technologies (CQT), National University of Singapore, Singapore 117543, Singapore.
Zeen SunCentre for Quantum Technologies (CQT), National University of Singapore, Singapore 117543, Singapore.ORCID 0009-0004-3760-1018
Leong Chuan KwekCentre for Quantum Technologies (CQT), National University of Singapore, Singapore 117543, Singapore.ORCID 0000-0002-0879-0591
Yuancheng ZhanCentre for Quantum Technologies (CQT), National University of Singapore, Singapore 117543, Singapore.ORCID 0000-0002-5473-3387

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantum cloning machines are essential in quantum information processing, finding applications in areas such as quantum communication and cryptographic protocols. However, the fidelity of universal quantum cloning machines diminishes as the dimension of the Hilbert space increases, resulting in significantly lower efficiency when cloning high-dimensional quantum states compared to qubits. In this study, we introduce a Hybrid Quantum Autocloning Machine (HQAM) that combines quantum autoencoding with universal quantum cloning. The core concept involves compressing a high-dimensional quantum state into a lower-dimensional effective subspace through a quantum autoencoder, conducting the cloning process within this reduced subspace, and then reconstructing the state in the original Hilbert space. Our results show that, for input states with a strong overlap with the effective qubit subspace, the HQAM achieves cloning fidelities exceeding the benchmark fidelity of direct qutrit universal cloning and approaching the optimal qubit cloning limit, while maintaining robustness under noise. These findings demonstrate that compression-assisted cloning provides a practical strategy for improving cloning performance in high-dimensional quantum systems and may enable more efficient quantum information processing protocols.

Indexed as

quantum autoencodingquantum cloningquantum information

Identifiers

PMID42187978
PMCPMC13205339

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.