Quantum Robotics

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Quantum Science and Technology".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 5488

Special Issue Editor


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Guest Editor
Dept Elect & Comp Engn, Portland State Univ, Portland, OR, USA
Interests: quantum computing; machine learning; intelligent robotics; system design; digital design; new technologies for computing

Special Issue Information

Dear Colleagues,

With increasing importance in Quantum Computing on one hand and Machine Learning on the other, combined with fast advancements in design of more and more sophisticated robots, it is obvious that at least some of the advanced future robots will be controlled by quantum computers. While the first ideas of quantum robots come from Benioff and are relatively old, there is so far no systematic definition of the unified research areas specifically related to quantum robotics. With a massive amount of data that will be generated by future sensors and rising complexity of control/planning/interacting/reasoning systems, as well as the fact that future robots will operate in all kinds of environments, especially interacting with humans, it is widely accepted that Quantum Algorithms and especially Quantum Machine Learning will play a major role in robotics and automated integrated systems. It is foreseeable that quantum algorithms, quantum sensors and quantum controls will be the main approach to next generation of robotics research. They will also bring new challenges to robotics community. This Special Issue will try to define and outline the nascent research area of Quantum Robotics. A Special Issue on Quantum Robotics of Applied Sciences journal (ISSN 2076-3417) is under preparation. Potential authors from both academia and industry are invited to submit outstanding and original unpublished research manuscripts and review articles on the state-of-the-art concepts and technologies as well as the latest findings in all aspects of Quantum Robotics.

While all related papers are of interest, papers that focus on specific applications of importance to future advanced robot systems will be given priority. Some of the most important areas include, but are not limited to:

 

• Quantum Robot Architectures

• Quantum Machine Learning

• Quantum Neural Networks

• Quantum Sensors

• Quantum Evolutionary Computing

• Quantum Algorithms for typical robotic tasks such as inverse kinematics

• Quantum Robot Vision and Perception

• Quantum Robot Planning and Motion Planning

• Quantum-Inspired Algorithms for Robotics

• Quantum Spectral Transforms, Wavelets and their Applications

• New ideas of using quantum phenomena (superposition, entanglement, teleportation, measurement) in robotics

• Quantum Creative Tasks

• Philosophical Issues

• Quantum Robots Reacting to various Environments

• Adaptive Quantum Robots

• Grover’s Search, Quantum Walks and QUBO in robotics applications

• Quantum Deep Learning

• Quantum Control, including control of systems using Heisenberg Model

• Quantum Kalman Filters

• Quantum Games for Robots

• Quantum Stochastic Systems

• Quantum Automata and Quantum Cellular Automata

• Lloyd and HHL Algorithms with robotics applications

• Quantum Principal Component Analysis

• Quantum Support Vector Machines

• Quantum Bayesian Networks

• Quantum Adiabatic and Quantum Topological Applications in robotics

• Theoretical Quantum Robots

• Quantum Nano-Robots

• Quantum Boosting

• New Ideas on Quantum Learning and Problem Solving

• Quantum Hidden Markov Models

• Quantum Simulators related to robotics

Prof. Dr. Perkowski Marek
Guest Editor

Manuscript Submission Information

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Keywords

  • Quantum Computer
  • Quantum Control
  • Quantum Machine Learning
  • Models of Quantum Computers
  • Quantum Robot Architectures

Published Papers (1 paper)

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Research

14 pages, 1761 KiB  
Article
Quantum Structure for Modelling Emotion Space of Robots
by Fei Yan, Abdullah M. Iliyasu, Sihao Jiao and Huamin Yang
Appl. Sci. 2019, 9(16), 3351; https://doi.org/10.3390/app9163351 - 15 Aug 2019
Cited by 9 | Viewed by 3607
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Quantum Robotics)
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