Continuously Monitored Quantum Sensors: Smart Tools and Applications

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Acquiring and interpreting data about physical processes is vital for science and technology. The targeted breakthrough of the project "Continuously Monitored Quantum Sensors: Smart Tools and Applications" (C'MON-QSENS!) is to develop tools to interpret data acquired from quantum sensors. Indeed, quantum-enhanced ultra-precise sensors are among the most disruptive quantum technologies with near-term applications in several disciplines, but with a limited reach so far. Most efforts are devoted to the measurement of static properties by single-shot or repeated measurement schemes, while many real-world applications are concerned with dynamical signals. Extracting information from time-series of data needs sensors operating in the continuously monitored regime, and here is where the interdisciplinary approach of C'MON-QSENS! emerges. We aim to develop continuously monitored hot atomic ensembles and optomechanical devices, and we pursue their application in a collaboration with leading experimentalists and theory researchers in quantum information theory, statistical inference and classical signal processing. We will create a unique synergy to close the interdisciplinary gap, so modern methods of (classical) signal processing and data inference can be incorporated within the context of quantum metrology. The result will allow advanced sensing tasks to be explicitly demonstrated in experiments. We will both gain a deeper understanding of quantum information processing in the real-time regime, and develop practical approaches to quantum sensing and interpretation of real-time signals. C'MON-QSENS! will advance the current frontiers of fundamental and applied knowledge on continuously monitored quantum systems by: A. Constructing advanced dynamical models to allow for an accurate description of real-time quantum sensors, including relevant decoherence mechanisms, non-linearities, sources of stochastic noise, and quantum back-action resulting from continuous-time measurements. B. Developing (i) signal processing and statistical inference techniques (Bayesian filtering, compressed sensing, sequential analysis) for highly controlled scenarios when the quantum sensor and signal dynamics can be accurately modelled, and (ii) model-free machine learning methods for real-world complex scenarios. This will advance fundamental theory on continuously monitored quantum systems and provide ultimate bounds on the performance for the relevant sensing tasks.C. Building quantum sensors based on continuously monitored atomic vapours and optomechanical systems. We will apply the dynamical models and inference techniques to optimize the sensors' operating regimes to allow tracking of real-life signals (e.g. neuron, brain, heart, and acceleration) and validate advanced sensing tasks such as wave-form estimation, model selection and change-point/anomaly detection.