Fuzzy quantum circuits to model emotional behaviors of humanoid robots

Raghuvanshi, Arushi; Perkowski, Marek

doi:10.1109/cec.2010.5586038

Cited by 18 publications

(24 citation statements)

References 12 publications

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“…Ongoing work considers modeling the behavior of agents using fuzzy quantum computations [8], and ranges from applications to develop QAs for quantum information.…”

Section: Resultsmentioning

confidence: 99%

Reduction and Decomposition Optimizations in Quantum Computing Simulation Applied to the Shor’s Algorithm

Ávila¹,

Reiser²,

Pilla³

2017

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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Abstract. Due to the expansion of transformations and read/write memory states by tensor products in multidimensional quantum applications, the exponential increase in temporal and spatial complexities constitutes one of the main challenges for quantum computing simulations. Simulation of these systems is important in order to develop and test new quantum algorithms. This work presents reduction and decomposition optimizations for the Distributed Geometric Machine environment. By exploring properties as the sparsity of the Identity operator and partiality of dense unitary transformations, better storage and distribution of quantum information are achieved. The main improvements are implemented by decreasing replication and void elements inherited from quantum operators. In the evaluation of this proposal, Shor's algorithm considering 2n+3 qubits in the order-finding quantum algorithm was simulated up to 25 qubits over CPU, sequentially and in parallel, and over GPU. Results confirm that temporal complexity is reduced. When comparing our implementations running on the same hardware with LIQUi| , academic release version, our new simulator was faster and allowed for the simulation of more qubits.

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“…Ongoing work considers modeling the behavior of agents using fuzzy quantum computations [8], and ranges from applications to develop QAs for quantum information.…”

Section: Resultsmentioning

confidence: 99%

Reduction and Decomposition Optimizations in Quantum Computing Simulation Applied to the Shor’s Algorithm

Ávila¹,

Reiser²,

Pilla³

2017

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

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“…After measurements, however, only one emotion will be observed. This is analogous to the human behaviour where in the human mind, there are numbers of emotions, but the actions typically represent one emotion [35].…”

Section: Quantum Effects In Human Behaviourmentioning

confidence: 95%

“…The notion of quantum circuits that correspond to affective behaviour is introduced by Perkowski et al in [35], i.e., to employ EPR (Einstein-Podolsky-Rosen) circuits to set qubits into an entangled state to influence the human behaviour. For instance, the first circuit in Fig.…”

Section: Quantum Effects In Human Behaviourmentioning

confidence: 99%

“…Following these insights, in [34], Lukac et al provided a quantum mechanical model of robotspecific emotions based on quantum cellular automata, where a robot is described as a set of functional agents each acting on behalf of its own emotional function. Years later, Raghuvanshi et al proposed concepts that apply quantum circuits to model fuzzy sets, which birthed a new method to model emotional behaviour for a humanoid robot [35]. More recently, in [36], Yan et al proposed a framework portraying emotion transition and fusion, which is encoded as quantum emotion space with three entangled qubit sequences to locate an emotion point within a three-dimensional emotion space.…”

Section: Introductionmentioning

confidence: 99%

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Conceptual Framework for Quantum Affective Computing and Its Use in Fusion of Multi-Robot Emotions

Yan¹,

Iliyasu²,

Hirota³

2020

Preprint

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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.

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“…To facilitate this, they described a robot as a set of functional agents, each acting on behalf of its own emotional function; hence, generating the whole robot emotional state. Years later, Raghuvanshi et al proposed the concepts that apply quantum circuits to model fuzzy sets, thereby creating a new method to model emotional behaviors for a humanoid robot [10]. This new method makes use of the entanglement and partial measurement properties from quantum mechanics available to the modeling of fuzzy systems to evolve behaviours of humanoid robots with a genetic algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

Quantum Structure for Modelling Emotion Space of Robots

et al. 2019

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Utilising the properties of quantum mechanics, i.e., entanglement, parallelism, etc., a quantum structure is proposed for representing and manipulating emotion space of robots. This quantum emotion space (QES) provides a mechanism to extend emotion interpretation to the quantum computing domain whereby fewer resources are required and, by using unitary transformations, it facilitates easier tracking of emotion transitions over different intervals in the emotion space. The QES is designed as an intuitive and graphical visualisation of the emotion state as a curve in a cuboid, so that an “emotion sensor” could be used to track the emotion transition as well as its manipulation. This ability to use transition matrices to convey manipulation of emotions suggests the feasibility and effectiveness of the proposed approach. Our study is primarily influenced by two developments. First, the massive amounts of data, complexity of control, planning and reasoning required for today’s sophisticated automation processes necessitates the need to equip robots with powerful sensors to enable them adapt and operate in all kinds of environments. Second, the renewed impetus and inevitable transition to the quantum computing paradigm suggests that quantum robots will have a role to play in future data processing and human-robot interaction either as standalone units or as part of larger hybrid systems. The QES proposed in this study provides a quantum mechanical formulation for quantum emotion as well as a platform to process, track, and manipulate instantaneous transitions in a robot’s emotion. The new perspective will open broad areas, such as applications in emotion recognition and emotional intelligence for quantum robots.

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Fuzzy quantum circuits to model emotional behaviors of humanoid robots

Cited by 18 publications

References 12 publications

Reduction and Decomposition Optimizations in Quantum Computing Simulation Applied to the Shor’s Algorithm

Reduction and Decomposition Optimizations in Quantum Computing Simulation Applied to the Shor’s Algorithm

Conceptual Framework for Quantum Affective Computing and Its Use in Fusion of Multi-Robot Emotions

Quantum Structure for Modelling Emotion Space of Robots

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