AI-assisted hyper-dimensional broadband quantum memory

• URL: http://arxiv.org/abs/2503.11098v2
• Date: Thu, 10 Apr 2025 12:02:40 GMT
• Title: AI-assisted hyper-dimensional broadband quantum memory
• Authors: Zeliang Wu, Jinxian Guo, Zhifei Yu, Wenfeng Huang, Chun-Hua Yuan, Weiping Zhang, L. Q. Chen,
• Abstract summary: We show efficient quantum memory for hyper-dimensional photons encoded with orbital angular momentum (OAM) and spin angular momentum (SAM)<n>OAM information is encoded from 5 to +5, combined with spin angular momentum encoding, enabling up to 22 dimensions.<n>Results demonstrate superior performance and potential applications in high-dimensional quantum information processing.
• Score: 0.7695660509846217
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-dimensional broadband quantum memory significantly expands quantum information processing capabilities, but the memory efficiency becomes insufficient when extended to high dimensions. We demonstrate an efficient quantum memory for hyper-dimensional photons encoded with orbital angular momentum (OAM) and spin angular momentum (SAM). OAM information is encoded from 5 to +5, combined with spin angular momentum encoding, enabling up to 22 dimensions. To ensure high memory efficiency, an artificial intelligence algorithm, a modified Differential Evolution (DE) algorithm using Chebyshev sampling, is developed to obtain a perfect signal-control waveform matching. Memory efficiency is experimentally achieved at 92% for single-mode Gaussian signal, 91% for information dimension of 6 and 80% for dimensional number to 22. The fidelity is achieved up to 99% for single-mode Gaussian signal, 96% for OAM information and 97% for SAM one, and 92% for whole hyper-dimensional signal, which is far beyond no-cloning limitation. Our results demonstrate superior performance and potential applications in high-dimensional quantum information processing. This achievement provides a crucial foundation for future quantum communication and quantum computing.

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