Linear Gaussian Quantum State Smoothing: Understanding the optimal unravelings for Alice to estimate Bob's state
Kiarn T. Laverick, Areeya Chantasri, Howard M. Wiseman

TL;DR
This paper advances the understanding of quantum state smoothing by deriving detailed equations for linear Gaussian states and proposing a hypothesis for Bob's optimal measurement strategy to maximize Alice's state purity improvement.
Contribution
It provides a detailed derivation of LGQ smoothing equations and introduces a hypothesis for Bob's optimal measurement strategy to enhance Alice's state estimation.
Findings
The hypothesis that Bob should observe the back-action from Alice's measurement is effective.
The derived equations clarify the role of Bob's measurement in quantum state smoothing.
The strategy improves the purity of Alice's estimated state compared to filtering alone.
Abstract
Quantum state smoothing is a technique to construct an estimate of the quantum state at a particular time, conditioned on a measurement record from both before and after that time. The technique assumes that an observer, Alice, monitors part of the environment of a quantum system and that the remaining part of the environment, unobserved by Alice, is measured by a secondary observer, Bob, who may have a choice in how he monitors it. The effect of Bob's measurement choice on the effectiveness of Alice's smoothing has been studied in a number of recent papers. Here we expand upon the Letter which introduced linear Gaussian quantum (LGQ) state smoothing [Phys. Rev. Lett., 122, 190402 (2019)]. In the current paper we provide a more detailed derivation of the LGQ smoothing equations and address an open question about Bob's optimal measurement strategy. Specifically, we develop a simple…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
