4 May 2018 Combining multi-photon entanglement, hyper-entanglement, and quantum networks for enhanced sensing
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Abstract
Quantum hyper-entanglement, multi-photon entanglement and quantum networks are combined to develop enhanced sensing capabilities. Quantum hyper-entanglement refers to entanglement in more than one degree of freedom, e.g. polarization, energy-time, orbital angular momentum (OAM), and frequency. Multi-photon entanglement involves entanglement typically between more than two photons, whereas hyper-entanglement is generally described as occurring between two photons referred to as the signal and ancilla photon. Multi-photon states representing an increasing hierarchy of robustness are discussed. These include N00N states, M&N states and linear combinations of M&N states. A M&N state, the (N, 1) states is shown to be very useful. A method of constructing M&N states using Shrodinger kitten states is considered. Multi-photon entanglement and hyper-entanglement are combined for enhanced sensing. This combined approach permits improvement in measures of effectiveness (MOEs) like SNR, signal to interference ratio (SIR), time-on-target (TOT), Holevo bound, and system range. The combined approach yields significant improvements in resolution both by permitting an effective reduction in wavelength used for measurement as well as decreasing the related Cramer Rao lower bound. This in turn permits sensors based on these concepts to have enhanced parameter estimation capabilities. These parameters can include range, bearing and elevation. Additional enhancements are found by combining the multi-photon hyper-entangled states with the idea of a quantum network. Quantum networks are a collection of nodes that may have quantum memory. This approach can significantly reduce loss; offer noise and interference resistance; decrease measurement error; and reduce size, weight, power and costs (SWAPC) for the overall system.
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James F. Smith, James F. Smith, } "Combining multi-photon entanglement, hyper-entanglement, and quantum networks for enhanced sensing", Proc. SPIE 10633, Radar Sensor Technology XXII, 1063316 (4 May 2018); doi: 10.1117/12.2302678; https://doi.org/10.1117/12.2302678
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