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.
Synthetic aperture radar (SAR) uses sensor motion to generate finer spatial resolution of a given target area. In this paper we explore the theoretical potential of quantum synthetic aperture quantum radar (QSAR). We provide theoretical analysis and simulation results which suggest that QSAR can provide improved detection performance over classical SAR in the high-noise low-brightness regime.
In this paper we discuss and examine approaches for detecting large objects in low-light maritime environments with a goal of improving the detection of large targets within a region of interest. More specifically, a passive ghost imaging system is proposed for using caustics illumination patterns to reconstruct a target image from correlations with intensities captured by a bucket detector.
In previous research we designed an interferometric quantum seismograph that uses entangled photon states to enhance sensitivity in an optomechanic device. However, a spatially-distributed array of such sensors, with each sensor measuring only nm-vibrations, may not provide sufficient sensitivity for the prediction of major earthquakes because it fails to exploit potentially critical phase information. We conjecture that relative phase information can explain the anecdotal observations that animals such as lemurs exhibit sensitivity to impending earthquakes earlier than can be done confidently with traditional seismic technology. More specifically, we propose that lemurs use their limbs as ground motion sensors and that relative phase differences are fused in the brain in a manner similar to a phased-array or synthetic-aperture radar. In this paper we will describe a lemur-inspired quantum sensor network for early warning of earthquakes. The system uses 4 interferometric quantum seismographs (e.g., analogous to a lemurs limbs) and then conducts phase and data fusion of the seismic information. Although we discuss a quantum-based technology, the principles described can also be applied to classical sensor arrays
The study of plate tectonic motion is important to generate theoretical models of the structure and dynamics of the Earth. In turn, understanding tectonic motion provides insight to develop sophisticated models that can be used for earthquake early warning systems and for nuclear forensics. Tectonic geodesy uses the position of a network of points on the surface of earth to determine the motion of tectonic plates and the deformation of the earths crust. GPS and interferometric synthetic aperture radar are commonly used techniques used in tectonic geodesy. In this paper we will describe the feasibility of interferometric synthetic aperture quantum radar and its theoretical performance for tectonic geodesy.
In the context of traditional radar systems, the Doppler effect is crucial to detect and track moving targets in the presence of clutter. In the quantum radar context, however, most theoretical performance analyses to date have assumed static targets. In this paper we consider the Doppler effect at the single photon level. In particular, we describe how the Doppler effect produced by clutter and moving targets modifies the quantum distinguishability and the quantum radar error detection probability equations. Furthermore, we show that Doppler-based delayline cancelers can reduce the effects of clutter in the context of quantum radar, but only in the low-brightness regime. Thus, quantum radar may prove to be an important technology if the electronic battlefield requires stealthy tracking and detection of moving targets in the presence of clutter.
Recent research suggests that quantum radar offers several potential advantages over classical sensing technologies. At present, the primary practical challenge is the fast and efficient generation of entangled microwave photons. To mitigate this limitation we propose and briefly examine a distributed architecture to synthetically increase the number of effectively-distinguishable modes.
The Radar Cross Section (RCS) is a crucial element for assessing target visibility and target characterization, and it depends not only on the target’s geometry but also on its composition. However, the calculation of the RCS is a challenging task due to the mathematical description of electromagnetic phenomena as well as the computational resources needed. In this paper, we will introduce two ideas for the use of quantum information processing techniques to calculate the RCS of dielectric targets. The first is to use toolboxes of quantum functions to determine the geometric component of the RCS. The second idea is to use quantum walks, expressed in terms of scattering processes, to model radar absorbing materials.
A major scientific thrust from recent years has been to try to harness quantum phenomena to increase the performance of a wide variety of information processing devices. In particular, quantum radar has emerged as an intriguing theoretical concept that could revolutionize electromagnetic standoff sensing. In this paper we will discuss how the techniques developed for quantum radar could also be used towards the design of novel seismographs able to detect small ground vibrations., We use a hypothetical earthquake warning system in order to compare quantum seismography with traditional seismographic techniques.
Sidelobe structures on classical radar cross section graphs are a consequence of discontinuities in the surface currents. In contrast, quantum radar theory states that sidelobe structures on quantum radar cross section graphs are due to quantum interference. Moreover, it is conjectured that quantum sidelobe structures may be used to detect targets oriented off the specular direction. Because of the high data bandwidth expected from quantum radar, it may be necessary to use sophisticated quantum signal analysis algorithms to determine the presence of stealth targets through the sidelobe structures. In this paper we introduce three potential quantum algorithmic techniques to compute classical and quantum radar cross sections. It is our purpose to develop a computer science-oriented tool for further physical analysis of quantum radar models as well as applications of quantum radar technology in various fields.
Quantum walks have been studied under several regimes. Motivated by experimental results on quantum
weak measurements and weak values as well as by the need to develop new insights for quantum algorithm
development, we are extending our knowledge by studying the behavior of quantum walks under the
regime of quantum weak measurements and weak values of pre- and postselected measurements (QWWM
hereinafter). In particular, we investigate the limiting position probability distribution and several statistical
measures (such as standard deviation) of a QWWM on an infinite line, and compare such results with
corresponding classical and quantum walks position probability distributions and statistical measures,
stressing the differences provided by weak measurements and weak values with respect to results
computed by using canonical observables.
We start by producing a concise introduction to quantum weak values and quantum weak measurements.
We then introduce definitions as well as both analytical and numerical results for a QWWM under
Hadamard evolution and extend our analysis to quantum evolution ruled by general unitary operators.
Moreover, we propose a definition and focus on the properties of mixing time of QWWM on an infinite line,
followed by a comparison of known corresponding results for classical and quantum walks mixing times. We
finish this paper by presenting a plausible experimental implementation of a QWWM.
We investigate the storage and retrieval of an image in a multi-particle quantum mechanical system. Several models are studied and compared with corresponding classical digital methods. We consider a situation in which qubits replace classical bits in an array of pixels and show several advantages. For example, we consider the situation in which 4 different values are randomly stored in a single qubit and show that quantum mechanical properties allow better reproduction of original stored values compared with classical (even stochastic) methods. The retrieval process is uniquely quantum (involves measurement in more than one bases). The independence and the finiteness of the stored copies of the image play an important role in the quantum protocol being better than the classical one. Other advantages of quantum storage of an image are found in its security.
Quantum computing, one of the most recent joint ventures between physics and the theory of computation, can be defined as the scientific field whose purpose is to develop hardware and algorithms based on quantum mechanical phenomena. In addition to further advance the mathematical and physical foundations of quantum computing, scientists and engineers who work in this field focus on developing cutting-edge quantum algorithms in areas like artificial intelligence, cryptanalysis, machine learning, database search, chemical simulations, and image processing. The course summarizes recent theoretical and experimental results that showcase the feasibility of large-scale quantum computation. In addition, the course describes the potential applications of quantum computing to signal analysis, sensor fusion, and computer vision.
Quantum sensors are sensing devices that exploit quantum phenomena in such a way that makes them perform substantially better than their classical counterparts. This course uses an information-theoretic approach to identify and explain the basic design principles and potential applications of quantum sensors. A primary goal of the course is to describe those aspects of quantum phenomena that can be harnessed in order to design and develop novel sensing devices. To this end, the course summarizes recent theoretical and experimental results that showcase the feasibility of quantum sensors. In addition, the course compares the theoretical performance of quantum sensors with their classical counterparts in the areas of radar, lidar, photo-detection, magnetometry, and gravimetry.