3:30–4:45 pm MCP 201
When two stars are too close to resolve in a conventional image, what can the collected light still tell us about their separation? This question illustrates a broader challenge in quantum sensing. The information we can extract depends on what we measure, how we combine signals from distant sensors, and how we protect those signals from noise. I will begin with a two-source model to show how spatial-mode measurements can reveal features below the diffraction limit and how background noise constrains this advantage. I will then use learning theory to identify which features can be recovered with a finite photon budget. Next, I will discuss how the pretty-good measurement can efficiently estimate a wave’s direction of arrival using signals collected across a distributed sensor array. Finally, I will explain when quantum error correction can suppress decoherence while preserving the signal, allowing precision to improve with longer interrogation times.