Since a few years, swarms of drones are used in both the civil and military domains. They might be composed of tens, hundreds if not thousands of drones (e.g. to achieve saturation effect). It thus becomes hard to deal with each of the individual drones that make it. Therefore, making a swarm a single object, that could then be controlled as a single entity, whatever its size and its surface, is the grail.Many projects have been working on this issue. Still, as far as we know, none has been convincingly successful. There are a number of reasons for that.From a conceptual point of view, the controls that make sense at the level of a swarm are not simply an upscale of the operations that make sense for the drones that compose it. Some controls are easy to upscale (take off, land) and to map back from the swarm to its individual components. Some are more difficult. For instance, what does it mean and does it even make sense to move a swarm to a given location? There is an underlying notion of synchronization that remains hidden if stating things so simply: when should the move begin, should the members go together, when is the move terminated, etc.? Whatever the answers to these questions, this requires stable communication and strong synchronization between the members of the swarm, which cannot be guaranteed in most theaters of operations, especially for large swarms. Additionally, there are operations that only make sense at the level of a swarm, not of an individual drone. For instance, it might be useful to split a swarm into two smaller sub swarms, and later reconnect them as a single object.From a technical point of view, two major configurations are to be considered. First, it the swarm is controlled by some central system on the ground (C2, Command and Control) it makes things partly tractable. The C2 has a global view of all the members of the swarm and the above issues can be dealt with, knowing the required information about each unitary system. Still this does not solve the problems of congestion, latences and disruptions. Second, if the swarm is composed of autonomous systems, i.e. when the swarm is composed of drones that decide on their own on their behavior, it becomes much more difficult. Still, this is the most interesting configuration in real world operation because autonomy makes it possible to address BLOS (Beyond Line of Sight) missions and avoids swarm to ground communication.The question to solve is what do we precisely expect when willing to make a swarm a single object? We claim that the perfect result would be to break the locality of each individual drone making the swarm a non-local object as if the drones were some sort of entangled (by analogy with quantum physics and the EPR principle). Therefore, our provocative claim that future swarms generation will be quantum swarms.
Since a few years, swarms of drones are used in both the civil and military domains. They might be composed of tens, hundreds if not thousands of drones (e.g. to achieve saturation effect). It thus becomes hard to deal with each of the individual drones that make it. Therefore, making a swarm a single object, that could then be controlled as a single entity, whatever its size and its surface, is the grail.Many projects have been working on this issue. Still, as far as we know, none has been convincingly successful. There are a number of reasons for that.From a conceptual point of view, the controls that make sense at the level of a swarm are not simply an upscale of the operations that make sense for the drones that compose it. Some controls are easy to upscale (take off, land) and to map back from the swarm to its individual components. Some are more difficult. For instance, what does it mean and does it even make sense to move a swarm to a given location? There is an underlying notion of synchronization that remains hidden if stating things so simply: when should the move begin, should the members go together, when is the move terminated, etc.? Whatever the answers to these questions, this requires stable communication and strong synchronization between the members of the swarm, which cannot be guaranteed in most theaters of operations, especially for large swarms. Additionally, there are operations that only make sense at the level of a swarm, not of an individual drone. For instance, it might be useful to split a swarm into two smaller sub swarms, and later reconnect them as a single object.From a technical point of view, two major configurations are to be considered. First, it the swarm is controlled by some central system on the ground (C2, Command and Control) it makes things partly tractable. The C2 has a global view of all the members of the swarm and the above issues can be dealt with, knowing the required information about each unitary system. Still this does not solve the problems of congestion, latences and disruptions. Second, if the swarm is composed of autonomous systems, i.e. when the swarm is composed of drones that decide on their own on their behavior, it becomes much more difficult. Still, this is the most interesting configuration in real world operation because autonomy makes it possible to address BLOS (Beyond Line of Sight) missions and avoids swarm to ground communication.The question to solve is what do we precisely expect when willing to make a swarm a single object? We claim that the perfect result would be to break the locality of each individual drone making the swarm a non-local object as if the drones were some sort of entangled (by analogy with quantum physics and the EPR principle). Therefore, our provocative claim that future swarms generation will be quantum swarms.
Quantum computing in drone technology begins to show potential big time, especially in the possibility of data processing, communication, and how to bring operational efficiency.In this study, the integration of quantum algorithms on drone navigation systems to enhance their efficiency in terms of security and performance is being investigated.However, in the interest of full disclosure, it should be noted that quantum-enabled drones have not been built and tested elsewhere to get an insight from a practical standpoint. This research is based on simulations and hypothetical models, which show that quantum algorithms could improve the optimization of drone operations.A simulation performed showed that the mean path length of data transmission was shortened by 12.4%, both from source to destination and back, compacting the whole process and increasing speed while it decreased packet loss by an average value close to 75% — when compared with regular methods. High-density urban obstacle avoidance efficiency increased by 26.1%. Though these results demonstrate an impressive theoretical advantage, realizing practical quantum computing on drones has many challenges to be overcome such as miniaturization of hardware components, power consumption and environmental protection. Overcoming these challenges is critical for future explorations into what quantum-integrated drones can achieve.This article presents a holistic overview of the current status of quantum technological advances in drones and sketches out what constitutes crucial steps to make things turn from theoretical calculations to real-life operational systems. Accordingly, this study sets a new foundation for future advancements in this nascent field and offers solutions that could get one step closer to realizing ambitious visions of autonomous aerial systems across multiple domains.
Theory and experiment have not fully resolved the apparent dichotomy , which has agonized physics for the past eighty years: on the one hand, the description of microsystems by quantum mechanics and, on the other, the description of macrosystems by classical dynamics or statistical mechanics. Derivations of the time-irreversible Liouville equation for an open quantum system, based on projecting out its environment, have narrowed the gap between the quantum and classical descriptions. Yet our `classical' intuition continues to be confronted by quantum-mechanical results like the Einstein--Podolsky--Rosen paradox that challenges the classical notion of locality, or the quantum Zeno effect which suggests that the isolation of a system is not the only way to preserve its quantum state. There are two key concepts in any discussion of such issues. The first, which is responsible for the most salient nonclassical properties, is entanglement , that is partial or complete correlation or, more generally, inseparability of the elements comprising a quantum ensemble . Even after their interaction has ceased, this inseparability, originating from their past interaction, can affect the state of one element when another element is subject to a nonunitary action, such as its measurement, tracing- out, or thermalization. The second key concept is decoherence of open quantum systems, which is the consequence of their entanglement with their environment, a `meter' or a thermal `reservoir', followed by the tracing-out of the latter. Despite new insights into entanglement and decoherence, there are still no complete, unequivocal answers to the fundamental questions of the transition from quantal to classical behaviour: how do irreversibility and classicality emerge from unitarity as systems and their environments become increasingly complex ? At what stage does system--meter entanglement give rise to a classical readout of the meter? Is there an upper limit on the size or complexity of systems displaying entanglement? Major developments have opened new vistas into controlled entanglement , which is the resource of quantum information processing. However, these developments have mainly focused on ensembles of simple two- and three-level systems that are thoroughly isolated from their environments. Treatments of coherence in quantum computing have mostly assumed that only a single or a few the elements of the quantum ensemble may simultaneously undergo an uncontrolled intervention---a quantum error. Decoherence-control protocols for more general types of errors are still incomplete. In order to resolve the outstanding issues of the quantum--classical transition, and study the control of entanglement and decoherence without the foregoing restrictions, we must venture into the domain of Quantum Complex Systems (QUACS), either consisting of a large number of inseparable elements or having many coupled degrees of freedom. Our conviction is that fundamental understanding and manipulation of entanglement within QUACS or their entanglement with the environment or a meter, call for the creation of a new conceptual framework or paradigm, that would encompass phenomena common to cold atoms in laser fields, large molecules, Josephson junctions, quantum gases and solids, with the view of employing these systems for quantum information processing and computing. Progress within this paradigm should allow us to answer the questions: does entanglement play an essential role in the evolution of large collections of complex systems? What are the size and complexity limits of systems and ensembles still controllable by an external intervention? What are the most appropriate decoherence protection schemes and control algorithms? This special issue addresses the challenge of understanding in depth and manipulating the basic quantum properties of optical, atomic, molecular and condensed-matter QUACS, and large ensembles thereof. In view of the interdisciplinary character of this issue, we find it expedient to look at the presented articles not only according to the physical objects they describe, but also from a unifying standpoint. Several unifying themes may be discerned: memory effects in dephasing and relaxation; protection of quantum information by its distribution throughout the ensemble; control of decoherence and entanglement by time-dependent interventions or measurements; manifestations of entanglement in scattering off statistical ensembles; measurment-induced dynamics. The concerted discussion of the topics outlined above should help us advance the new paradigm that addresses our abilities to diagnose and manipulate the entangled states of complex quantum objects and their robustness against decoherence. These abilities are required for quantum information (QI) applications in matter--wave interferometry in molecular, semiconducting or superconducting systems. On the fundamental level, this book may help establish the notion of dynamical information exchange between quantum systems and chart in detail the route from unitarity to classicality. Further developments within the outlined paradigm should yield novel, advantageous QI processing schemes and high-sensitivity interferometers, owing to decoherence suppression and effective control of many degrees of freedom. In the long run, these strategies may prove to be the first step towards the (hitherto unattempted) use of entanglement in nanotechnology, metrology and chemistry, with potentially remarkable novel applications.
Swarms of drones are utilized in a wide range of applications, considering that they can be deployed on-demand and are economically affordable. Furthermore, they can also have a significant role in the creation of future Quantum Networks (QNs). As a matter of fact, the use of drones allows deploying a non terrestrial Quantum Metropolitan Area Network (QMAN), overcoming Optical Fibers’ (OFs) limits, due to the large percentage of photons that scatters before reaching the receiver. However, random fluctuations of drones’ positions and atmospheric turbulence can affect the quality of the Free Space Optic (FSO) link with a significant impact on performance. Considering that Quantum Drone Networks (QDNs) require significant control, Software-Defined Networking (SDN) paradigm can play a key role in their provisioning. Specifically, an SDN Controller is responsible for managing the global strategies for the distribution of end-to-end (E2E) entangled pairs. Therefore, this paper provides the design of an SDN-based architecture for supporting high-performance Metropolitan Quantum Drone Networks (MQDNs) with a specific protocol for creating entanglement between two Ground Stations (GSs) through the swarm of drones. The proposed architecture can be employed for distributed quantum computing applications and entanglement-based Quantum Key Distribution (QKD) services. Moreover, a suited objective function to optimize the planning and operation of the swarm mission has been proposed. Finally, the paper provides a performance evaluation considering the most relevant metrics, such as fidelity, entanglement rate, and the overhead of the proposed protocol, pointing out that even higher performance than OFs is achievable.
Quantum computing is a branch of computer science derived from the fundamental laws of quantum mechanics, such as state superposition, multi-value logic and destructive measure. An open challenge in itself is to re-think in quantum terms classic problems and solving techniques. Another nature-inspired field is the development of swarm-based robotic applications, where the challenge is catching the fundamental laws governing swarm dynamics, such as pattern formation and target reaching. Here, we review some recent approaches on swarm dynamics, the organizational rules of which are formalized according to quantum computing. In this way, the shades of probability in decision-making for multiple-robot systems can be expressed according to the multi-value logic underlying quantum computing. In our review, specific quantum circuits are sketched to give an idea of how these problems have been faced in computational terms. The article is enriched by references to sonification, a strategy adding one more sensory dimension to data representation, a human-friendly tool to navigate the complexity of swarm-robotic movements in a given arena.This article is part of the theme issue 'The road forward with swarm systems'.
This paper demonstrates and proves that the coordination of actions in a distributed swarm can be enhanced by using quantum entanglement. In particular, we focus on• Global and local simultaneous random walks, using entangled qubits that collapse into the same (or opposite) direction, either random direction or totally controlled simultaneous movements.• Identifying eavesdropping from malicious eavesdroppers aimed at disturbing the simultaneous random walks by using entangled qubits that were sent at random or with predefined bases.
Unmanned Aerial Vehicles (UAVs) have gone from single platforms that were remotely piloted to fully networked, autonomous cyber-physical systems that operate in the civilian, industrial and defense arenas. Their increasing reliance on wireless communication for command and control, cooperative sensing, distributed decision-making and swarm coordination has raised the level of communication security to the point where it has become a mission-critical requirement. Contemporary UAV security architectures are much based on classical public-key–based cryptography (RSA and elliptic are the most well-known) whose security assumptions become fundamentally vulnerable against the presence of large-scale quantum computing. This chapter gives a comprehensive and future-oriented discussion about post-quantum cryptography (PQC)-based security models for UAVs communication and information exchange networks. Quantum resistance should be applied not only at the algorithmic level but across the whole UAV communications trust stack, which is the main argument. Part I set the conceptual and technical ground by affecting a short range of the improvement of UAV communication systems, in articulating a system quantum-era threat model and giving a motivation for mission-adaptive, cross-layer security architectures. The discussion makes the long-term confidentiality, survivability of the system and continuing operation more robust, and the security of UAV with PQC enabled is no longer speculative, but is considered a strategic requirement.