If we augment part of a classical network with quantum capabilities, what can be done better than in the purely classical setting? In this talk, we will see two use cases in optical networks where a quantum augmentation could be beneficial: 1) localizing transmission loss change; and 2) learning link transmissivities. The ability to localize transmission loss change in optical networks is crucial for maintaining network reliability, performance and security. Quantum probes, implemented by sending blocks of n coherent-state pulses augmented with continuous-variable (CV) squeezing (n = 1) or weak temporal-mode entanglement (n > 1) over a lossy channel to a receiver with homodyne detection capabilities, are known to be more sensitive than their quasi-classical counterparts in detecting a sudden increase in channel loss. Assuming a subset of network nodes can send and receive such probes, I will present a scheme that combines these quantum probes with classical frameworks of Boolean network tomography and Quickest Change Detection. This combination of techniques leads to a quantifiable asymptotic quantum speedup for localizing transmission drop. In the second part of the talk, I will show that the same set of quantum probes, when applied to learning link transmissivities, may lead to a smaller error-ellipsoid volume. However, this observation is only preliminary.

Yufei Zheng is a postdoc at UMass Amherst, working with Don Towsley. She completed her PhD in the Department of Computer Science at Princeton University, where she was advised by Jennifer Rexford. Prior to that, she spent some time in Technion working on enumerative combinatorics. Yet another pivot in research focus came when she became interested in quantum computing during the fifth year of her PhD. Her recent research has focused on quantum-augmented networks, and she is broadly interested in finding quantum advantage wherever they may arise.