Classical Optimization Techniques for Quantum Detection

Abstract: The problem of discriminating among non-orthogonal quantum states arises throughout quantum information processing. For example, in quantum communications, a message is encoded as a quantum state and transmitted over a quantum channel to a receiver that decodes the message. In quantum sensing, a photon in a known state is emitted to gather information about the environment from its reflections. Underlying state discrimination are the design of the quantum measurement device used for measurement and the design of the hypotheses used to encode information or probe the environment. These are the core degrees of freedom in quantum information processing system design. This talk will present the design of three quantum detection tasks, including quantum state and channel discrimination over uncertain channels, sequential unambiguous quantum state and channel discrimination, and quickest quantum change-point detection. These designs make use of a range of classical non-convex optimization methods, including stochastic, bi-level, and multi-objective optimization.
 

Bio: Xiaodong Wang received his Ph.D. in Electrical Engineering from Princeton University. He is a Professor of Electrical Engineering at Columbia University. His research interests span wireless communications, signal processing, machine learning, and quantum information processing. He received the 1999 NSF CAREER Award, the 2001 IEEE Communications Society and Information Theory Society Joint Paper Award, and the 2011 IEEE Communications Society Award for Outstanding Paper on New Communication Topics. He has served as an Associate Editor for the IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, IEEE Transactions on Signal Processing, and IEEE Transactions on Information Theory.