Experimental demonstration of reconstructing quantum states with generative models

• URL: http://arxiv.org/abs/2407.15102v1
• Date: Sun, 21 Jul 2024 09:44:05 GMT
• Title: Experimental demonstration of reconstructing quantum states with generative models
• Authors: Xuegang Li, Wenjie Jiang, Ziyue Hua, Weiting Wang, Xiaoxuan Pan, Weizhou Cai, Zhide Lu, Jiaxiu Han, Rebing Wu, Chang-Ling Zou, Dong-Ling Deng, Luyan Sun,
• Abstract summary: We report an experimental demonstration of reconstructing quantum states based on neural network generative models with an array of programmable superconducting transmon qubits. Our results experimentally showcase the intriguing potential for exploiting machine learning techniques in validating and characterizing complex quantum devices.
• Score: 0.44600863117978684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum state tomography, a process that reconstructs a quantum state from measurements on an ensemble of identically prepared copies, plays a crucial role in benchmarking quantum devices. However, brute-force approaches to quantum state tomography would become impractical for large systems, as the required resources scale exponentially with the system size. Here, we explore a machine learning approach and report an experimental demonstration of reconstructing quantum states based on neural network generative models with an array of programmable superconducting transmon qubits. In particular, we experimentally prepare the Greenberger-Horne-Zeilinger states and random states up to five qubits and demonstrate that the machine learning approach can efficiently reconstruct these states with the number of required experimental samples scaling linearly with system size. Our results experimentally showcase the intriguing potential for exploiting machine learning techniques in validating and characterizing complex quantum devices, offering a valuable guide for the future development of quantum technologies.

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