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IEEE International Conference on Quantum Computing and Engineering (QCE)

Towards a Quantum Modeling Approach to Reactive Agents

Abderrafìâa Koukam, Abdeljalil Abbas‐Turki, Vincent Hilaire, Yassine Ruichek

Published Oct 1, 2021• 14 citations
Publisher

Abstract

Quantum computing offers a new approach to the problem modeling and solving. This paper deals with the quantum modeling of reactive agents. It also proposes a quantum algorithm to implement the subsumption architecture, widely used by reactive agents, particularly in robotics. This work shows the contribution of the formalism proposed by quantum mechanics to the modeling and the proof of certain properties of the agent behavior. After, the definition of the reactive agent state modeling, the paper suggests a behavior modeling approach based on two steps for subsumption architecture. The first one models the preset behavior that links each action to the perception states. The second one determines, among several actuated actions, the one that the robot must achieve. The subsumption architecture raises the challenge of modeling hierarchical priority of actions. To this end, a multipartite entanglement is used in the second step. More precisely, the paper proposes and generalizes a W-state circuit in order to be used for modeling hierarchical priority actions and controlling the robot accordingly. The result of both steps provides a formal model that links the robot’s perception (input) to the actions (output), with respect to the subsumption architecture. The proposed model of agent is simulated using IBM quantum computer. The simulation shows that the model can either be served as a control unit of the robot (CU) to obtain the suitable action or to simulate the robot behavior.

DOI
10.1109/qce52317.2021.00029

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Quantum computing offers a new approach to the problem modeling and solving. This paper deals with the quantum modeling of reactive agents. It also proposes a quantum algorithm to implement the subsumption architecture, widely used by reactive agents, particularly in robotics. This work shows the contribution of the formalism proposed by quantum mechanics to the modeling and the proof of certain properties of the agent behavior. After, the definition of the reactive agent state modeling, the paper suggests a behavior modeling approach based on two steps for subsumption architecture. The first one models the preset behavior that links each action to the perception states. The second one determines, among several actuated actions, the one that the robot must achieve. The subsumption architecture raises the challenge of modeling hierarchical priority of actions. To this end, a multipartite entanglement is used in the second step. More precisely, the paper proposes and generalizes a W-state circuit in order to be used for modeling hierarchical priority actions and controlling the robot accordingly. The result of both steps provides a formal model that links the robot’s perception (input) to the actions (output), with respect to the subsumption architecture. The proposed model of agent is simulated using IBM quantum computer. The simulation shows that the model can either be served as a control unit of the robot (CU) to obtain the suitable action or to simulate the robot behavior.
The possibility of integrating quantum computation in a traditional system appears to be a viable route to drastically improve the performance of systems endowed with artificial intelligence. An example of such processing consists of implementing a teleo-reactive system employing quantum computing. In this work, we considered the navigation of a robot in an environment where its decisions are drawn from a quantum algorithm. In particular, the behavior of a robot is formalized through a production system. It is used to describe the world, the actions it can perform, and the conditions of the robot’s behavior. According to the production rules, the planning of the robot activities is processed in a recognize–act cycle with a quantum rule processing algorithm. Such a system aims to achieve a significant computational speed-up.
Today's robots are controlled with classical computers that make decisions based on algorithms designed for a classical computing architecture.However, the amount of data increases continuously -a fact which pushes classical computers that control these robots to their limits.In comparison to classical computing, fundamentally different concepts of physics are the basis of quantum computing and thereby quantum computing has the potential to solve specific computational problems more efficiently.In addition to the aforementioned complications, the subgroup of robots that are subject to the research of this paper, namely humanoid robots, not only need more computational capacity but also require an algorithmic design based on a behavioral model which avoids causing antipathy when interacting with human beings.Using a real quantum computer with quantum phenomena such as superposition, entanglement, and quantum parallelism, new behavioral models and algorithms can be designed to improve human robot interaction and speed up machine learning processes.This paper promotes a hybrid-controlled robot using the combination of a classical computer-and quantum computer.With four quantum circuits to define a behavioral model and three different quantum machine learning algorithms we show the advantage of the hybrid-controlled robot.
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 TTT that gives the single time step dynamics is associated with each task. T=Ta+Tc{T=T}_{a}{+T}_{c}T=Ta​+Tc​ is a sum of action phase and computation phase step operators. Conditions that Ta{T}_{a}Ta​ and Tc{T}_{c}Tc​ 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.
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( N2)N^2)N2) in classical robots to O( NN)N\sqrt N)NN​) . 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.
Formalisms based on quantum theory have been used in Cognitive Science for decades due to their descriptive features. A quantum-like (QL) approach provides descriptive features such as state superposition and probabilistic interference behavior. Moreover, quantum systems dynamics have been found isomorphic to cognitive or biological systems dynamics. The objective of this paper is to study the feasibility of a QL perception model for a robot with limited sensing capabilities. We introduce a case study, we highlight its limitations, and we investigate and analyze actual robot behaviors through simulations, while actual implementations based on quantum devices encounter errors for unbalanced situations. In order to investigate QL models for robot behavior, and to study the advantages leveraged by QL approaches for robot knowledge representation and processing, we argue that it is preferable to proceed with simulation-oriented techniques rather than actual realizations on quantum backends.
In this paper, we propose the concepts that apply quantum circuits to model Fuzzy Sets, thereby creating a new method to model behaviors for a humanoid robot. It is similar to the standard fuzzy sets from two points of view -1) of measured probabilities of state |1〉, and 2) that of the normalized measurement in a quantum ensemble computer. However, one can also look at the internal quantum states created by the operators of the new model. This extends fuzzy sets from [0, 1] interval to some other domain, similar to complex fuzzy logic, but also brings superposed, entangled and parallel quantum behaviors. The standard fuzzy logic aspect (external) of the new model maps the numbers in interval [0,1] to points on one meridian of the Bloch Sphere and after measurement, the results map back to numbers from interval [0,1]. However, the other, more important, aspect of this model (internal) is the operation on arbitrary quantum states, and the realization of additional states via phase measurements. We discuss and illustrate how this model can be used to represent a hidden state of reasoning agents or an emotional state of a humanoid robot.