Coupling one controllable qubit to a bosonic sensor gives a provable exponential reduction in the number of measurements needed to learn Fourier coefficients and temporal correlations of classical signals, with a 10^7-fold improvement demonstrated experimentally.
Optimal Measurement of Field Properties with Quantum Sensor Networks
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abstract
We consider a quantum sensor network of qubit sensors coupled to a field $f(\vec{x};\vec{\theta})$ analytically parameterized by the vector of parameters $\vec\theta$. The qubit sensors are fixed at positions $\vec{x}_1,\dots,\vec{x}_d$. While the functional form of $f(\vec{x};\vec{\theta})$ is known, the parameters $\vec{\theta}$ are not. We derive saturable bounds on the precision of measuring an arbitrary analytic function $q(\vec{\theta})$ of these parameters and construct the optimal protocols that achieve these bounds. Our results are obtained from a combination of techniques from quantum information theory and duality theorems for linear programming. They can be applied to many problems, including optimal placement of quantum sensors, field interpolation, and the measurement of functionals of parametrized fields.
fields
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Exponential quantum advantage for learning signals with a single qubit
Coupling one controllable qubit to a bosonic sensor gives a provable exponential reduction in the number of measurements needed to learn Fourier coefficients and temporal correlations of classical signals, with a 10^7-fold improvement demonstrated experimentally.