Quantum Robots Research Articles

At The Intersection of Medical Robotic Surgery and Drug Discovery with Quantum Computing

Classical natural language processing (NLP) has been successfully modeled and implemented to enable robots or chatbots to have some understanding of human languages and communication, but this has not been very effective to help the robots communicate effectively. Quantum computers introduce a multitude of new ways in which more highly complex and integrated robotics modeling and computations can be achieved, delivering finer control in robotics use cases with humans. As mentioned in articles “Genome assembly using quantum and quantum‑inspired annealing” “The pandemic highlighted the importance of and breadth of robotics applications, shining a light on a potential robotic future in healthcare, which we discuss here”, there is a lot more to explore in quantum computing. However, in this study, we will present how quantum robotics can help in developing superior surgical techniques where robots perform advanced medical surgeries, with a more sophisticated, faster, less intrusive process that achieves more successful patient outcomes in complex surgery processes that might otherwise take repeat surgeries with multiple human medical teams. This approach will include the use of quantum natural language processing (QNLP) [1,2]

Quantum Mechatronics

Mechatronics systems, a macroscopic domain, aim at producing highly efficient engineering platforms, with applications in a variety of industries and situations. On the other hand, quantum technologies, a microscopic domain, are emerging as a promising avenue to speed up computations and perform more efficient sensing. Recently, these two fields have started to merge in a novel area: quantum mechatronics. In this review article, we describe some developments produced so far in this respect, including early steps into quantum robotics, macroscopic actuators via quantum effects, as well as educational initiatives in quantum mechatronics.

Quantum robotics: A scientometrics assessment of global publications during 1993-2020

The study examines global research on Quantum Robotics on quantitative and qualitative metrics to understand the status of research in the subject at the global, national, institutional, and individual level. The analysis is based on 650 publications as indexed in the Scopus database for the period 1993-2020. The study provides a window to most productive countries, organizations and authors in the subject, provides a distribution of publications across broad subjects, identifies top modes of research communication, and describes the characteristics of highly-cited papers. The quantum robotics research registered a 33.92% annual average growth, received an average of 9.47 citations per paper (CPP) since publication during 1993-2020, which was 16.31 CPP in 1993-2006 and 8.53 CPP in 2007-2020. Only 12 of the 650 publications (1.85% share) have registered as highly cited papers. China and the USA lead the world ranking with a global publication share of 21.38% and 17.38% respectively during the period.

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Neuroscience and brain-inspired artificial intelligence are significant research areas. Many countries have launched brain-related projects in which neuroscience and brain-inspired artificial intelligence are major targeted areas to increase national interests and enhance their strength in key areas such as military and homeland security in the competitive global world. Methods, emerging technologies, and progress in neuroscience and brain-inspired artificial intelligence are introduced in this paper that specifically include brain-inspired computing, brain association graph, brain networks, the connectome, brain reconstruction, imaging technologies used for the brain, chips and devices inspired by the human brain, brain-computer interface or brain-machine interfaces, cyborg, neuro-robotics, and quantum robotics. Challenges in some of the topics are also presented and discussed.

Quantum robots perturbed by Levy processes: Stochastic analysis and simulations

In the present work, the problem of quantization of a robot via the Schrodinger equation with Jerk Levy noise and the wave function plots is analyzed. Quantum average and quantum fluctuation of the trajectory positions are displayed. It is observed that the quantum robots are molecules whose link angles can be varied resulting in changing chemical properties, thus justifying the importance of this work also in physics of biological engineering. we have also made computations on the statistics of the wave function as well as transition probabilities between two stationary states. The quantum analysis has been based on using time independent perturbation theory to evaluate the approximate evolution operator of the unperturbed robot.

Automation of quantum Braitenberg vehicles using finite automata: Moore machines

Since the advent of quantum computation, there have been attempts to apply quantum mechanics to robotics and develop quantum robots. In this paper, we discuss the working of classical Braitenberg vehicles and the various problems which lead us to propose a novel improvement by automating it using classical finite automata, Moore machines. We then improve by introducing an intrinsic nature to it such that it stops its motion without requiring external signals, by using entanglement. This leads to our design of a quantum automated Braitenberg vehicle which we improve by incorporating the possibility of external control over its movement. We implement the circuits in IBM quantum experience and obtain results matching our theoretical predictions. This paper makes the following contributions: an experimental verification of the quantum logic with reasonably good results despite decoherence and errors in quantum gate applications, the idea of introducing intrinsic behaviour using quantum mechanics, the idea of flexibility in developing manual external controls, and achieving better results than classical robots using lesser number of gates.

Quantum Structure for Modelling Emotion Space of Robots

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.

Quantum robots can fly; play games: an IBM quantum experience

Quantum Robot is an excellent future application that can be achieved with the help of a quantum computer. As a practical example, quantum controlled Braitenberg vehicles proposed by Raghuvanshi et al. [Proceedings of the 37th International Symposium on Multiple-Valued Logic (2007)] is a mobile quantum system and hence acts as a quantum robot. Braitenberg vehicles are simple circuit robots which can experience natural behaviours like fear, aggression and love etc. These robots can be controlled by quantum circuits incorporating quantum principles such as entanglement and superposition. Complex behaviours can be mimicked by a quantum circuit that can be implemented in a quantum robot. Here we investigate the scheme of Raghuvanshi et al. and propose a new quantum circuit to make the quantum robot fly. We demonstrate one of its application in playing a game. The quantum robot we present here shows the behaviour of `fear' and its movement is deterministic in nature. This phenomenon can be successfully modelled in a game, where it can always avoid accident. The proposed quantum circuit is designed in IBM quantum experience describing the above protocol.

Quantum eigenlogic observables applied to the study of fuzzy behaviour of Braitenberg vehicle quantum robots

PurposeThe purpose of this paper is to apply the quantum “eigenlogic” formulation to behavioural analysis. Agents, represented by Braitenberg vehicles, are investigated in the context of the quantum robot paradigm. The agents are processed through quantum logical gates with fuzzy and multivalued inputs; this permits to enlarge the behavioural possibilities and the associated decisions for these simple vehicles.Design/methodology/approachIn eigenlogic, the eigenvalues of the observables are the truth values and the associated eigenvectors are the logical interpretations of the propositional system. Logical observables belong to families of commuting observables for binary logic and many-valued logic. By extension, a fuzzy logic interpretation is proposed by using vectors outside the eigensystem of the logical connective observables. The fuzzy membership function is calculated by the quantum mean value (Born rule) of the logical projection operators and is associated to a quantum probability. The methodology of this paper is based on quantum measurement theory.FindingsFuzziness arises naturally when considering systems described by state vectors not in the considered logical eigensystem. These states correspond to incompatible and complementary systems outside the realm of classical logic. Considering these states allows the detection of new Braitenberg vehicle behaviours related to identified emotions; these are linked to quantum-like effects.Research limitations/implicationsThe method does not deal at this stage with first-order logic and is limited to different families of commuting logical observables. An extension to families of logical non-commuting operators associated to predicate quantifiers could profit of the “quantum advantage” due to effects such as superposition, parallelism, non-commutativity and entanglement. This direction of research has a variety of applications, including robotics.Practical implicationsThe goal of this research is to show the multiplicity of behaviours obtained by using fuzzy logic along with quantum logical gates in the control of simple Braitenberg vehicle agents. By changing and combining different quantum control gates, one can tune small changes in the vehicle’s behaviour and hence get specific features around the main basic robot’s emotions.Originality/valueNew mathematical formulation for propositional logic based on linear algebra. This methodology demonstrates the potentiality of this formalism for behavioural agent models (quantum robots).

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

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.

Progress and Challenges of Neuroscience and Brain-inspired Artificial Intelligence

Neuroscience and brain-inspired artificial intelligence are significant research areas. Many countries have launched brain-related projects in which neuroscience and brain-inspired artificial intelligence are major targeted areas to increase national interests and enhance their strength in key areas such as military and homeland security in the competitive global world. Methods, emerging technologies, and progress in neuroscience and brain-inspired artificial intelligence are introduced in this paper that specifically include brain-inspired computing, brain association graph, brain networks, the connectome, brain reconstruction, imaging technologies used for the brain, chips and devices inspired by the human brain, brain-computer interface or brain-machine interfaces, cyborg, neuro-robotics, and quantum robotics. Challenges in some of the topics are also presented and discussed.

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

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.

Quantum Robotics: A Primer on Current Science and Future Perspectives

Quantum robotics is an emerging engineering and scientific research discipline that explores the application of quantum mechanics, quantum computing, quantum algorithms, and related fields to robotics. This work broadly surveys advances in our scientific understanding and engineering of quantum mechanisms and how these developments are expected to impact the technical capability for robots to sense, plan, learn, and act in a dynamic environment. It also discusses the new technological potential that quantum approaches may unlock for sensing and control, especially for exploring and manipulating quantum-scale environments. Finally, the work surveys the state of the art in current implementations, along with their benefits and limitations, and provides a roadmap for the future.

Molecular quantum robotics: particle and wave solutions, illustrated by "leg-over-leg" walking along microtubules.

Remarkable biological examples of molecular robots are the proteins kinesin-1 and dynein, which move and transport cargo down microtubule “highways,” e.g., of the axon, to final nerve nodes or along dendrites. They convert the energy of ATP hydrolysis into mechanical forces and can thereby push them forwards or backwards step by step. Such mechano-chemical cycles that generate conformal changes are essential for transport on all different types of substrate lanes. The step length of an individual molecular robot is a matter of nanometers but the dynamics of each individual step cannot be predicted with certainty (as it is a random process). Hence, our proposal is to involve the methods of quantum field theory (QFT) to describe an overall reliable, multi–robot system that is composed of a huge set of unreliable, local elements. The methods of QFT deliver techniques that are also computationally demanding to synchronize the motion of these molecular robots on one substrate lane as well as across lanes. Three different challenging types of solutions are elaborated. The impact solution reflects the particle point of view; the two remaining solutions are wave based. The second solution outlines coherent robot motions on different lanes. The third solution describes running waves. Experimental investigations are needed to clarify under which biological conditions such different solutions occur. Moreover, such a nano-chemical system can be stimulated by external signals, and this opens a new, hybrid approach to analyze and control the combined system of robots and microtubules externally. Such a method offers the chance to detect mal-functions of the biological system.

Quantum robot: structure, algorithms and applications

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 the system structure, several learning control algorithms, including quantum searching algorithms and quantum reinforcement learning algorithms, are presented for quantum robots. The theoretical results show that quantum robots using quantum searching algorithms can reduce the complexity of the search problem from O($N^2)$ in classical robots to O($N\sqrt N)$. Simulation results demonstrate that quantum robots are also superior to classical robots in efficient learning under novel quantum reinforcement learning algorithms. Considering the advantages of quantum robots, some important potential applications are also analyzed and prospected.

Quantum robots and environments

Quantum robots and their interactions with environments of quantum systems are described, and their study justified. A quantum robot is a mobile quantum system that includes an on-board quantum computer and needed ancillary systems. Quantum robots carry out tasks whose goals include specified changes in the state of the environment, or carrying out measurements on the environment. Each task is a sequence of alternating computation and action phases. Computation phase activites include determination of the action to be carried out in the next phase, and recording of information on neighborhood environmental system states. Action phase activities include motion of the quantum robot and changes in the neighborhood environment system states. Models of quantum robots and their interactions with environments are described using discrete space and time. A unitary step operator $T$ that gives the single time step dynamics is associated with each task. ${T=T}_{a}{+T}_{c}$ is a sum of action phase and computation phase step operators. Conditions that ${T}_{a}$ and ${T}_{c}$ should satisfy are given along with a description of the evolution as a sum over paths of completed phase input and output states. A simple example of a task---carrying out a measurement on a very simple environment---is analyzed in detail. A decision tree for the task is presented and discussed in terms of the sums over phase paths. It is seen that no definite times or durations are associated with the phase steps in the tree, and that the tree describes the successive phase steps in each path in the sum over phase paths.

Some foundational aspects of quantum computers and quantum robots

This paper addresses foundational issues related to quantum computing. The need for a universally valid theory such as quantum mechanics to describe to some extent its own validation is noted. This includes quantum mechanical descriptions of systems that do theoretical calculations (i.e. quantum computers) and systems that perform experiments. Quantum robots interacting with an environment are a small first step in this direction. Quantum robots are described here as mobile quantum systems with on-board quantum computers that interact with environments. Included are discussions on the carrying out of tasks and the division of tasks into computation and action phases. Specific models based on quantum Turing machines are described. Differences and similarities between quantum robots plus environments and quantum computers are discussed.