
Efficient Approximate Quantum State Tomography with Basis Dependent NeuralNetworks
We use a metalearning neuralnetwork approach to predict measurement ou...
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Modeling and interpolation of the ambient magnetic field by Gaussian processes
Anomalies in the ambient magnetic field can be used as features in indoo...
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Optimising experimental design in neutron reflectometry
Using the Fisher information (FI), the design of neutron reflectometry e...
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Dense Quantum Measurement Theory
Quantum measurement is a fundamental cornerstone of experimental quantum...
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Detection of Dangerous Magnetic Field Ranges from Tablets by Clustering Analysis
The paper considers the problem of the extremely low frequency magnetic ...
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NeuralNetwork Heuristics for Adaptive Bayesian Quantum Estimation
Quantum metrology promises unprecedented measurement precision but suffe...
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Resourceefficient adaptive Bayesian tracking of magnetic fields with a quantum sensor
By addressing single electron spins through Ramsey experiments, nitrogenvacancy centres can act as highresolution sensors of magnetic field. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be minimised. Bayesian estimation and adaptive experiment optimisation protocols work by computing the probability distribution of the magnetic field based on measurement outcomes and, by computing aquisition settings for the next measurement. These protocols can speed up the sensing process by reducing the number of measurements required. However, the computations feeding into the next iteration measurement settings must be performed quickly enough to allow realtime updates. This paper addresses the issue of computational speed by implementing an approximated Bayesian estimation technique, where probability distributions are approximated by a superposition of Gaussian functions. Given that only three parameters are required to fully describe a Gaussian, we find that the magnetic field probability distribution can typically be described by fewer than ten numbers, achieving a reduction in the number of operations by factor 20 compared to existing approaches, allowing for faster processing.
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