Quantum Robotics, Neural Networks and the Quantum Force Interpretation

Prateek Tandon, Stanley Lam, Ben Shih +3 more

Carlos Pedro Gonçalves

A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM's five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target's dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target's dynamics accordingly.

G Gayathri, Aravind Sethuraman, Vishaal K Anna

Abstract In this paper, the study represents a theoretical conceptualization on the potential improvements to interpret and devise under the notion of quantum mechanics & AI utilizing psychology for social robots. The framework is elaborated in regard to the development of emotions encoded through information and possibilities, which manipulates the use of transitions of these emotions in states through the Quantum & AI measurements. To support the development, work introduced is an interpretation of quantum mechanics, linked with quantum science, systems science, and Conceptualized Neural Network. The understanding of these emotions are expected to impact the technical capability of robots in a specific aspect to sense, capture, and act in an environment relied on optimization of quantum bits, where a robot, perform these emotions through a network which will allow interaction with the given target, changing the robot’s state of response according to optimization and gates from target itself, computing the robot’s emotional performance based on measurement and mathematical expressions changing in accordance to the available information. This method will help to emphasize its viability and effectiveness in the synthesis of emotional intellect in social robots.

Fei Yan, Abdullah M. Iliyasu, Nianqiao Li +2 more

Mehdi Fazilat, Nadjet Zioui

This research explores the application of quantum-inspired neural networks (QNNs) to address the inverse kinematics problem in robotic arms, explicitly focusing on the ABB IRB140, an articulated robot arm with six degrees of freedom. The primary objective is to develop a quantum-inspired activation function for multilayer perceptron (MLP) neural networks. The study evaluates their performance in avoiding singularities by comparing artificial neural networks (ANNs) with quantum neural networks (QNNs). The findings demonstrate that QNNs outperform ANNs in terms of mean absolute error (MAE), achieving a 15.60% lower MAE in the model without singularities and a 16.67% reduction in the Jacobian-based MAE in the model designed to avoid singularities. The study demonstrates that QNNs offer superior accuracy in predicting the robot arm's inverse kinematics, achieving a position error of 1.64 mm and an orientation error of 0.00179 radians while effectively avoiding singularities. These outcomes underscore the potential of quantum-inspired neural networks to enhance robotic arm manipulations' precision, efficiency, and performance.

M. V. Altaĭsky, N. E. Kaputkina