Luis Bugalho

Quantum sensors are powerful tools for measuring physical quantities with high sensitivity, enabling, for instance, the mapping of Earth’s gravitational field , detecting very small changes of magnetic fields, or the passage of time. The underlying principle is to use a quantum state as a probe that interacts with the physical quantity of interest, thereby encoding relevant information into the state. Although individual quantum sensors may exhibit remarkable sensitivity, the precision of a certain measurement can be significantly enhanced when multiple probes are entangled. Distributed quantum sensing extends this further and leverages entanglement among spatially separated sensors, allowing them to function as a single, coherent system. This approach enables measurements across extended spatial regions, while surpassing the precision achievable by independent sensors. However, a significant challenge in a network setting is ensuring that sensors deployed across different parties serve as the necessary resources for the correct functioning of the target sensing task. This challenge has motivated the combination of quantum cryptography with quantum sensing. In this context, Shettell et al. introduced the notion of privacy for sensor networks, ensuring that, beyond the metrological advantage of cooperative estimation of a global function, parties can also maintain the privacy of their local information and control what data is accessible to others. In this work, we adopt this protocol and focus on a multi-user quantum sensor network framework to analyze the privacy aspects of this parameter estimation task, leveraging a high-quality four-party GHZ state source.

Analytical and algebraic geometry are valuable tools for dealing with problems involving analytical functions and polynomials. In what we connote as spatial quantum sensing the goal is, given an underlying field and a set of quantum sensors interrogating the field in a set of positions, to find an estimator for some property the field. This property can have multiple forms, be it distinguishing the source of a target signal, or evaluating the field (or a derivative thereof) in an arbitrary position. In this work we also link this problem to the development of networks of quantum sensors, and the role and usefulness of entangling these sensors. We find that the estimators that come out as a solution to the problem are such that a non-local entangled strategy provides maximum precision. We start by working under the assumption of polynomial fields, which relates to the interpolation problem, and then generalize for any signal that is modeled via analytical functions, giving rise to any general least-squares estimator. We discuss the effects of the placement of the sensors in the estimation, namely, how to find well defined, construction error-free placements for the sensors. In the case of interpolation we provide concrete examples and proofs in a $m$-dimensional array of sensors, and discuss necessary and sufficient conditions for the more general cases. We provide clear examples of the possible use-cases and statements, and compare a non-local entangled strategy with the best local strategy for an interpolation problem, showing the benefit in terms of precision in a distributed sensing scenario. This is a key tool for a wide-range of problem in sensing problems, ranging from large-scale such as earth-sized experiments, to local-scale, such has biological experiments.