Speaker: Huangjun Zhu (Fudan University)
Time: May 19th, Monday, 2025, 14:00h-15:00h
Location: 1310, SIMIS
Zoom Meeting ID: 479 937 5280 (Passcode: SIMIS)
Abstract
Shadow estimation is a sample-efficient protocol for learning the properties of a quantum system using randomized measurements, but the current understanding on qudit shadow estimation is quite limited compared with the qubit setting. Here we clarify the sample complexity of qudit shadow estimation based on the Clifford group, where the local dimension d is an odd prime. Notably, we show that the overhead of qudit shadow estimation over the qubit counterpart is only O(d), which is independent of the qudit number n, although the set of stabilizer states may deviate exponentially from a 3-design with respect to the third moment operator. Furthermore, by adding one layer of magic gates, we propose a simple circuit that can significantly boost the efficiency. Actually, a single magic gate can already eliminate the O(d) overhead in qudit shadow estimation and bridge the gap from the qubit setting. In addition, in thrifty shadow estimation based on the Clifford group, the variance is inversely correlated with the degree of nonstabilizerness, which is a key resource in quantum information processing. For fidelity estimation, it decreases exponentially with the stabilizer 2-R´enyi entropy of the target state, which endows the stabilizer 2-R´enyi entropy with a clear operational meaning.
About speaker
Prof. Huangjun Zhu got Bachelor, Master, and PhD degrees from Zhejiang University, Peking University, and National University of Singapore, respectively. After postdoctoral research at Perimeter Institute and Cologne Institute for Theoretical Physics, he joined Department of physics,
Fudan University in January 2018. His main research interest is quantum information theory, including quantum measurements, quantum characterization, verification, and validation (QCVV), entanglement theory, and blind quantum computation etc.
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