Quantum Data Fusion

Marco Lanzagorta (US Naval Research Laboratory)

RESUMEN: Sensor networks offer improved performance through the use of data fusion algorithms that combine the data from different nodes to provide optimal target information. On the other hand, quantum sensors are devices that harness quantum phenomena to increase their sensing performance. In this talk we discuss two potential areas of intersection between Quantum Information Technologies and Information Fusion. The first area we call "Quantum (Data Fusion)" and refers to the use of quantum computers to perform data fusion algorithms with classical data generated by quantum and classical sensors. As we will discuss, we expect that these quantum fusion algorithms will have a better computational complexity than traditional fusion algorithms. This means that quantum computers could allow the efficient fusion of large data sets for complex multi-target tracking. On the other hand, "(Quantum Data) Fusion" refers to the fusion of quantum data that is being generated by quantum sensors. The output of the quantum sensors is considered in the form of qubits, and a quantum computer performs data fusion algorithms. In other words, the sensor network becomes a quantum computer where the physical interaction to be measured acts as a quantum computational oracle. Our theoretical models suggest that we can expect that these algorithms can increase the sensitivity of the quantum sensor network.