Optical Fiber Communication Conference

Achieving High-Efficiency Error-Correcting Transmission of Qutrits with On-Chip Quantum Autoencoder

Denghui Wang, Haoran Ma, Donghui Chen, Liao Ye, Fanjie Ruan, Yuehai Wang, Jianyi Yang

Published Jan 1, 2025• 0 citations

Abstract

We designed a programmable quantum autoencoder on a silicon photonic chip with nearly no compression loss, which enabled us to propose and implement a new highly efficient error-correcting transmission protocol for qutrits.

DOI: 10.1364/ofc.2025.th2a.56

Related papers

Efficient Qutrit Transmission with Error Correction Using On-chip Quantum Autoencoders

Denghui Wang, Haoran Ma, Donghui Chen +5 more

We designed a programmable quantum autoencoder on a silicon photonic chip with nearly no compression loss, which enables us to propose and implement a new highly efficient error-correcting transmission protocol for qutrits.

Jan 1, 20250 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-OpticsJan 1, 20223 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:1810.01637Physical Review LettersFeb 11, 201998 citations

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

Hui Zhang, Lingxiao Wan, Tobias Haug +15 more

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.

Science AdvancesOct 7, 202242 citations

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, 20212 citations

Generating hypergraph states on a silicon-photonic chip

Jieshan Huang, Xudong Li, Xiaojiong Chen +2 more

We experimentally create complete classes of twenty-nine four-qubit hypergraph states on a programmable silicon-photonic chip, and implement measurement-based protocols on hypergraph states, which provide an alternative resource for quantum information processing.

Jan 1, 20230 citations