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Adaptive Quantum State Tomography with Active Learning
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DOI：10.22331/q-2023-10-09-1129.png)
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摘要
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Recently, tremendous progress has been made in the field of quantum science and technologies: different platforms for quantum simulation as well as quantum computing, ranging from superconduct-ing qubits to neutral atoms, are start-ing to reach unprecedentedly large sys-tems. In order to benchmark these sys-tems and gain physical insights, the need for efficient tools to characterize quantum states arises. The exponential growth of the Hilbert space with system size ren-ders a full reconstruction of the quantum state prohibitively demanding in terms of the number of necessary measurements. Here we propose and implement an ef-ficient scheme for quantum state tomog-raphy using active learning. Based on a few initial measurements, the active learn-ing protocol proposes the next measure-ment basis, designed to yield the max-imum information gain. We apply the active learning quantum state tomogra-phy scheme to reconstruct different multi-qubit states with varying degree of entan-glement as well as to ground states of the XXZ model in 1D and a kinetically con-strained spin chain. In all cases, we obtain a significantly improved reconstruction as compared to a reconstruction based on the exact same number of measurements and measurement configurations, but with ran-domly chosen basis configurations. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and character-izing quantum devices, e.g. for quantum simulation, and paves the way for scalable adaptive protocols to probe, prepare, and manipulate quantum systems.
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IF:
5.4
论文数:
974
被引数:
1.0W
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