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A brand-new paradigm of robots-quantum robots-is proposed through the fusion of quantum theory with robot technology. A quantum robot is essentially a complex quantum system which generally consists of three fundamental components: multi-quantum computing units (MQCU), quantum controller/actuator, and information acquisition units. Corresponding to...
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... quantum robots have communication interfaces to exchange information with distant mainframes or other quantum robots, which can constitute a multi quantum robot system as shown in Fig. 2. With external communication, quantum information can be exchanged and the advantages of quantum communication such as high channel capability, perfect security and quantum teleportation can be fully ...
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... In [12], Dong et al. presented the structural background of a quantum robot including description of its fundamental components and frameworks for multi-quantum computing units (MQCU), quantum actuator, and information acquisition units. Based on this structure, a quantum robot uses the information acquisition units to perceive its environment and acquire information, and then send sensing information to the MCQU. ...
... Moreover, quantum robots have communication interfaces to exchange information with distant mainframes or other quantum robots, which can constitute a multi quantum robot system as shown in Fig. 4. With external communication, quantum information can be exchanged and the advantages of quantum communication such as high channel capability, perfect security and quantum teleportation can be fully utilized [12]. These features could be also used to facilitate the fusion of emotion for multi-robots in a communication scenario, which will be discussed in Section 4. ...
... In this regard, a quantum sensor is a kind of microstructural sensor, that is designed by applying quantum effect. To detect faint classical signals (external stimulus), as presented in [12], two types of quantum sensors, superconduction quantum interference device sensor [14] and quantum well Hall sensor [15] can be considered. The EC transforms and controls the quantum emotion signal using quantum operators, i.e., quantum gates. ...
This study presents a modest attempt to interpret, formulate, and manipulate emotion of robots within the precepts of quantum mechanics. Our proposed framework encodes the emotion information as a superposition state whilst unitary operators are used to manipulate the transition of the emotion states which are recovered via appropriate quantum measurement operations. The framework described provides essential steps towards exploiting the potency of quantum mechanics in a quantum affective computing paradigm. Further, the emotions of multi-robots in a specified communication scenario are fused using quantum entanglement thereby reducing the number of qubits required to capture the emotion states of all the robots in the environment, and fewer quantum gates are needed to transform the emotion of all or part of the robots from one state to another. In addition to the mathematical rigours expected of the proposed framework, we present a few simulation-based demonstrations to illustrate its feasibility and effectiveness. This exposition is an important step in the transition of formulations of emotional intelligence to the quantum era.
... With quantum computing techniques, new approaches to solve those challenges but also new fields of research are on the horizon. While quantum computers are in theory capable of performing all kinds of calculations, it is not to assume that there will be computers (or robots for that matter) that are entirely quantum-powered even tough quantum robots have been proposed in literature [4], [5]. Instead, there will be quantum computing cloud services initially and potentially quantum co-processors (QPUs) that work together with classical CPUs. ...
Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.
... Also, quantum counting (Brassard et al. 1998) and amplitude estimation (Brassard et al. 2000) algorithms use the amplitude amplification mechanism in various points in their procedures. Dong et al. (2005Dong et al. ( , 2006Dong et al. ( , 2008 proposed a reinforcement learning algorithm based on the mathematics of quantum theory. In this work, inspired by the superposition principle, a value updating algorithm is introduced. ...
... In this work, inspired by the superposition principle, a value updating algorithm is introduced. The authors also 1 3 introduced a quantum robot (Dong et al. 2006) that explores in an unknown environment with the help of the proposed quantum learning algorithm. ...
Trust models play an important role in decision support systems and computational environments in general. The common goal of the existing trust models is to provide a representation as close as possible to the social phenomenon of trust in computational domains. In recent years, the field of quantum decision making has been significantly developed. Researchers have shown that the irrationalities, subjective biases, and common paradoxes of human decision making can be better described based on a quantum theoretic model. These decision and cognitive theoretic formulations that use the mathematical toolbox of quantum theory (i.e., quantum probabilities) are referred to by researchers as quantum-like modeling approaches. Based on the general structure of a quantum-like computational trust model, in this paper, we demonstrate that a quantum-like model of trust can define a powerful and flexible trust evolution (i.e., updating) mechanism. After the introduction of the general scheme of the proposed model, the main focus of the paper would be on the proposition of an amplitude amplification-based approach to trust evolution. By performing four different experimental evaluations, it is shown that the proposed trust evolution algorithm inspired by the Grover’s quantum search algorithm is an effective and accurate mechanism for trust updating compared to other commonly used classical approaches.
... This bias could exactly be an explanation for the functioning of the human brain leading to automated aspects of conceptual reasoning such as 'the disjunction and conjunction effects'. The above analysis is highly relevant for representations of genuine cognitive models in technology, for example as attempted in artificial intelligence and robotics [37]- [39]. ...
We identify the presence of typically quantum effects, namely 'superposition'
and 'interference', in what happens when human concepts are combined, and
provide a quantum model in complex Hilbert space that represents faithfully
experimental data measuring the situation of combining concepts. Our model
shows how 'interference of concepts' explains the effects of underextension and
overextension when two concepts combine to the disjunction of these two
concepts. This result supports our earlier hypothesis that human thought has a
superposed two-layered structure, one layer consisting of 'classical logical
thought' and a superposed layer consisting of 'quantum conceptual thought'.
Possible connections with recent findings of a 'grid-structure' for the brain
are analyzed, and influences on the mind/brain relation, and consequences on
applied disciplines, such as artificial intelligence and quantum computation,
are considered.
... Since the physical realization of QRL mainly needs Hadamard gates and phase gates and both of them are relatively easy to be implemented in quantum computation, our work also presents a new task to implement QRL using practical quantum systems for quantum computation and will simultaneously promote related experimental research [51]. Once QRL becomes realizable on real physical systems, it can be effectively used to quantum robot learning for accomplishing some significant tasks [52], [53]. ...
... The two research fields have rapidly grown so that it gives birth to the combining of traditional learning algorithms and quantum computation methods, which will influence representation and learning mechanism, and many difficult problems could be solved appropriately in a new way. Moreover, this idea also pioneers a new field for quantum computation and artificial intelligence [52], [53], and some efficient applications or hidden advantages of quantum computation are probably approached from the angle of learning and intelligence. ...
The key approaches for machine learning, especially learning in unknown probabilistic environments are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum par-allelism, a framework of value updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is parallelly updated according to rewards. Some related characteristics of QRL such as convergence, optimality and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speed up learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given and the results demonstrate the effectiveness and superiority of QRL algorithm for some complex problems. The present work is also an effective exploration on the application of quantum computation to artificial intelligence. Index Terms— quantum reinforcement learning, state super-position, collapse, probability amplitude, Grover iteration.
... Quantum robot has been introduced by Benioff [14,15,16,17] and is described as a quantum system exploiting the superposition and the entanglement of the state of the robot with the state of its environment. Recently in [18] presented are quantum robots with respect to the quality and speed of decision making using the Grover quantum search algorithm [13]. Unlike in these works, here the focus is not on how the robot is implemented with respect to its environment, but rather on the strategies for learning the robotic behaviors based on quantum circuit structures. ...
In this paper studied are new concepts of robotic behaviors-determin-istic and quantum probabilistic. In contrast to classical circuits, the quantum circuit can realize both of these behaviors. When applied to a robot, a quantum circuit controller realizes what we call quantum robot behaviors. We use automated methods to synthesize quantum behaviors (circuits) from the examples (examples are cares of the quantum truth table). The don't knows (minterms not given as examples) are then converted not only to deterministic cares as in the classical learning, but also to output values generated with various probabilities. The Occam Razor principle, fundamental to inductive learning, is satisfied in this approach by seeking circuits of reduced complexity. This is illustrated by the synthesis of single output quantum circuits, as we extended the logic synthesis approach to Inductive Machine Learning for the case of learning quantum circuits from behavioral examples.
This chapter is intended to be an introduction to quantum mechanics and computing for surgeons and non-computer scientists. It explains the basis of quantum computers through an understanding of the principles of quantum mechanics and theory. Differences between classical computing and quantum computing are highlighted. Building upon the previous chapter, the potential for applying such quantum computing systems within a cloud framework toward the development of future digital operating theaters and next-generation surgical robots is discussed.
A qualitative control method using reinforcement learning (RL) and grey system is developed for mobile robot navigation in an unknown environment. New representation and computation mechanisms are key approaches for learning and control problems with incomplete information or in large probabilistic environments. In this paper, the uncertainties in sensor information and qualitative localization are represented using grey systems. Traditional RL methods are also combined with grey theory for mobile robot navigation control and a new grey reinforcement learning (GRL) method is proposed to solve complex problems where environment information is incomplete and grey models are constructed through representing incomplete information with grey algebra. The experimental results verify the effectiveness and superiority of the qualitative control system and the presented approaches also show alternative ways for the measurement and learning control problems with incomplete information of the environment.
