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Science China Physics Mechanics and Astronomy

Demonstration of controlled high-dimensional quantum teleportation

Min-Yu Lv, Xiao‐Min Hu, Neng-Fei Gong, Tie-Jun Wang, Yu Guo, Bi‐Heng Liu, Yun‐Feng Huang, Chuan‐Feng Li, Guang‐Can Guo

Published Jan 25, 2024• 43 citations
Publisher
DOI
10.1007/s11433-023-2286-8

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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.
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.