Computer Science Speaking Skills Talk July 12, 2023 3:00pm — 4:00pm Location: In Person - Reddy Conference Room, Gates Hillman 4405 Speaker: XINYU WU , Ph.D. Student, Computer Science Department, Carnegie Mellon University https://www.andrew.cmu.edu/user/xinyuw1/ Learning quantum states using few qubits Learning properties of quantum systems is important for many applications, for instance, the verification of quantum technologies. Consider a scenario where one can obtain independent samples of an unknown probability distribution, and the task is to learn the expectation of some function under the unknown distribution. Quantum shadow tomography is the quantum analogue of this quantum problem. In quantum terms, this problem becomes learning the value of an unknown quantum state measured with respect to some observable, given access to copies of the unknown state. In contrast to the classical scenario, in a quantum scenario one can make entangled measurements across multiple copies of the unknown state. However, large entangled measurements require having sufficient quantum memory, which could be a limiting factor in near-term quantum computers. Hence, it can be useful to analyze algorithms that only make measurements on a limited number of states simultaneously. In this talk, I will discuss results about shadow tomography with limited quantum memory, primarily focusing on a paper of Chen, Cotler, Huang and Li. I will also give an overview on current work, joint with Ryan O’Donnell. Presented in Partial Fulfillment of the CSD Speaking Skills Requirement. Add event to Google Add event to iCal
