Abstract

Quantum state tomography is the fundamental physical task of learning a complete classical description of an unknown state of a quantum system given coherent access to many identical samples of it. The complexity of this task is commonly characterised by its sample-complexity: the minimal number of samples needed to reach a certain target precision of the description. While the sample complexity of quantum state tomography has been well studied, the memory complexity has not been investigated in depth. Indeed, the bottleneck in the implementation of naïve sample-optimal quantum state tomography is its massive quantum memory requirements. In this work, we propose and analyse a quantum state tomography algorithm which retains sample-optimality but is also memory-efficient. Our work is built on a form of unitary Schur sampling and only requires streaming access to the samples.

Personal information

Yanglin Hu is a PhD candidate in the Centre for Quantum Technologies at the National University of Singapore. His research focuses on two aspects, multi-partite quantum cryptographic primitives and applications of Schur-Weyl duality in quantum information theory. Yanglin obtained a Master of Science from the Department of Physics at ETH Zurich, and a Bachelor of Science from the Department of Physics at Peking University.

Please join with the link:

Sample Optimal and Memory Efficient Quantum State Tomography |加入會議 |Microsoft Teams