In this paper 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 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. Our theoretical models suggest that we expect that these algorithms can increase the sensitivity of the quantum sensor network.
Marco Lanzagorta, Oliverio Jitrik, Jeffrey Uhlmann, and Salvador E. Venegas-Andraca, "Data fusion in entangled networks of quantum sensors," Proc. SPIE 10200, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVI, 102000M (Presented at SPIE Defense + Security: April 11, 2017; Published: 2 May 2017); https://doi.org/10.1117/12.2262661.
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