© 2026 Aqora Quantum S.A.S.

Terms Privacy Legal Notice

Science Advances

Resource-efficient high-dimensional subspace teleportation with a quantum autoencoder

Hui Zhang, Lingxiao Wan, Tobias Haug, Wai‐Keong Mok, Stefano Paesani, Yuzhi Shi, Hong Nian Cai, L. K. Chin, Muhammad Faeyz Karim, Limin Xiao, Xianshu Luo, Feng Gao, Bin Dong, Syed M. Assad, M. S. Kim, Anthony Laing, L. C. Kwek, A. Q. Liu

Published Oct 7, 2022• 42 citations

Abstract

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.

DOI: 10.1126/sciadv.abn9783

Related papers

Chip-to-Chip High-Dimensional Teleportation via A Quantum Autoencoder

Hui Zhang, Lingxiao Wan, Tobias Haug +14 more

Abstract Quantum teleportation transfers unknown quantum states from one node in a quantum network to another. It is one of the crucial architectures in quantum information processing. The teleportation of high-dimensional quantum states remains challenging due to the difficulties in executing high-dimensional Bell state measurement. Here, we propose a Quantum Autoencoder-Facilitated Teleportation (QAFT) protocol for high-dimensional quantum teleportation, and report the first demonstration of QAFT on qutrits using an integrated photonic platform for future scalability. The key strategy is to reduce the dimension of the input states by erasing redundant information and reconstruct its initial state after chip-to-chip teleportation. Machine learning is applied in training the autoencoder to facilitate the teleportation of any state from a particular high-dimensional subspace and achieve the reconstruction of the unknown state (by the decoder) with high fidelities (~ 0.971). Experimentally, we teleport unknown qutrits by generating, transferring and manipulating photons, and training quantum autoencoders on a silicon chip. A teleportation fidelity of ~ 0.894 is demonstrated. Our scheme opens pathway towards quantum internet and cryptography to transfer unmeasured states in a quantum computer. It also lays the groundwork for machine learning technologies in quantum networks and quantum computations.

•Aug 20, 2021•2 citations

On-Chip Quantum Autoencoder for Teleportation of High-Dimensional Quantum States

Hui Zhang, Lingxiao Wan, Tobias Haug +4 more

Currently most quantum teleportation experiments are based on qubits. Here, we demonstrate a quantum autoencoder assisted teleportation for high-dimensional quantum states. Our method of training the autoencoder allows us to take a finite sample of those states, learn how to compress them to qubits with nearly unit fidelity. High fidelity is achieved between the input qutrit and the qutrit recovered from the teleported qubit.

Conference on Lasers and Electro-Optics•Jan 1, 2022•3 citations

Experimental Realization of a Quantum Autoencoder: The Compression of Qutrits via Machine Learning

Alex Pepper, Nora Tischler, Geoff J. Pryde

With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the dataset allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.

arXiv:1810.01637•Physical Review Letters•Feb 11, 2019•98 citations

Quantum autoencoders for efficient compression of quantum data

Jonathan Romero, Jonathan P Olson, Alán Aspuru‐Guzik

Abstract Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x , to map x to a lower dimensional point y such that x can likely be recovered from y . The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particular data set of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.

arXiv:1612.02806•Quantum Science and Technology•Aug 17, 2017•559 citations

Autoencoding-Assisted Quantum Cloning Machine

Qian Jun Beh, Moritz Straeter, Zeen Sun +2 more

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.

Entropy•May 18, 2026•0 citations

Rigorous noise reduction with quantum autoencoders

Wai‐Keong Mok, Hui Zhang, Tobias Haug +7 more

Reducing noise in quantum systems is a significant challenge in advancing quantum technologies. We propose and demonstrate a noise reduction scheme utilizing a quantum autoencoder, which offers rigorous performance guarantees. The quantum autoencoder is trained to compress noisy quantum states into a latent subspace and eliminate noise through projective measurements. We identify various noise models in which the noiseless state can be perfectly reconstructed, even at high noise levels. We apply the autoencoder to cool thermal states to the ground state and reduce the cost of magic state distillation by several orders of magnitude. Our autoencoder can be implemented using only unitary transformations without the need for ancillas, making it immediately compatible with state-of-the-art quantum technologies. We experimentally validate our noise reduction methods in a photonic integrated circuit. Our results have direct applications in enhancing the robustness of quantum technologies against noise.

arXiv:2308.16153•AVS Quantum Science•May 31, 2024•4 citations

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.

Abstract Quantum teleportation transfers unknown quantum states from one node in a quantum network to another. It is one of the crucial architectures in quantum information processing. The teleportation of high-dimensional quantum states remains challenging due to the difficulties in executing high-dimensional Bell state measurement. Here, we propose a Quantum Autoencoder-Facilitated Teleportation (QAFT) protocol for high-dimensional quantum teleportation, and report the first demonstration of QAFT on qutrits using an integrated photonic platform for future scalability. The key strategy is to reduce the dimension of the input states by erasing redundant information and reconstruct its initial state after chip-to-chip teleportation. Machine learning is applied in training the autoencoder to facilitate the teleportation of any state from a particular high-dimensional subspace and achieve the reconstruction of the unknown state (by the decoder) with high fidelities (~ 0.971). Experimentally, we teleport unknown qutrits by generating, transferring and manipulating photons, and training quantum autoencoders on a silicon chip. A teleportation fidelity of ~ 0.894 is demonstrated. Our scheme opens pathway towards quantum internet and cryptography to transfer unmeasured states in a quantum computer. It also lays the groundwork for machine learning technologies in quantum networks and quantum computations.

With quantum resources a precious commodity, their efficient use is highly desirable. Quantum autoencoders have been proposed as a way to reduce quantum memory requirements. Generally, an autoencoder is a device that uses machine learning to compress inputs, that is, to represent the input data in a lower-dimensional space. Here, we experimentally realize a quantum autoencoder, which learns how to compress quantum data using a classical optimization routine. We demonstrate that when the inherent structure of the dataset allows lossless compression, our autoencoder reduces qutrits to qubits with low error levels. We also show that the device is able to perform with minimal prior information about the quantum data or physical system and is robust to perturbations during its optimization routine.

Abstract Classical autoencoders are neural networks that can learn efficient low-dimensional representations of data in higher-dimensional space. The task of an autoencoder is, given an input x , to map x to a lower dimensional point y such that x can likely be recovered from y . The structure of the underlying autoencoder network can be chosen to represent the data on a smaller dimension, effectively compressing the input. Inspired by this idea, we introduce the model of a quantum autoencoder to perform similar tasks on quantum data. The quantum autoencoder is trained to compress a particular data set of quantum states, where a classical compression algorithm cannot be employed. The parameters of the quantum autoencoder are trained using classical optimization algorithms. We show an example of a simple programmable circuit that can be trained as an efficient autoencoder. We apply our model in the context of quantum simulation to compress ground states of the Hubbard model and molecular Hamiltonians.

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.

Reducing noise in quantum systems is a significant challenge in advancing quantum technologies. We propose and demonstrate a noise reduction scheme utilizing a quantum autoencoder, which offers rigorous performance guarantees. The quantum autoencoder is trained to compress noisy quantum states into a latent subspace and eliminate noise through projective measurements. We identify various noise models in which the noiseless state can be perfectly reconstructed, even at high noise levels. We apply the autoencoder to cool thermal states to the ground state and reduce the cost of magic state distillation by several orders of magnitude. Our autoencoder can be implemented using only unitary transformations without the need for ancillas, making it immediately compatible with state-of-the-art quantum technologies. We experimentally validate our noise reduction methods in a photonic integrated circuit. Our results have direct applications in enhancing the robustness of quantum technologies against noise.