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International Journal of Humanoid Robotics

Quantum-Inspired Bidirectional Associative Memory for Human–Robot Communication

Naoki Masuyama, Chu Kiong Loo, Naoyuki Kubota

Published Jun 1, 2014• 0 citations
Publisher

Abstract

The emerging research area of a quantum-inspired computing has been applied to various field such as computational intelligence, and showed its superior abilities. However, most existing researches are focused on theoretical simulations, and have not been implemented in systems under practical environment. For human–robot communication, associative memory becomes essential for multi-modal communication. However, it always suffers from low memory capacity and recall reliability. In this paper, we propose a quantum-inspired bidirectional associative memory with fuzzy inference. We show that fuzzy inference satisfies basic postulates of quantum mechanics, but also learning algorithm for weight matrix in associative memory. In addition, we construct a communication system with robot partner using proposed model. This is the first successful attempt to overcome conventional problems in associative memory model with a robot application.

DOI
10.1142/s0219843614500066

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The emerging research area of a quantum-inspired computing has been applied to various field such as computational intelligence, and showed its superior abilities. However, most existing researches are focused on theoretical simulations, and have not been implemented in systems under practical environment. For human–robot communication, associative memory becomes essential for multi-modal communication. However, it always suffers from low memory capacity and recall reliability. In this paper, we propose a quantum-inspired bidirectional associative memory with fuzzy inference. We show that fuzzy inference satisfies basic postulates of quantum mechanics, but also learning algorithm for weight matrix in associative memory. In addition, we construct a communication system with robot partner using proposed model. This is the first successful attempt to overcome conventional problems in associative memory model with a robot application.
Quantum-Inspired Computational Intelligence based on quantum postulates is an emerging research area that exploit the parallelism of quantum mechanics. However, existing research efforts are limited to theoretical simulations and has not been implemented in robot application. With regards as Human-Robot Interaction, associative memory become essential for mutual communication. However, associative memory always suffers from limited memory capacity and high sensitivity to noise and the ability to recall from multi-modal sensory inputs. In this paper, we propose a Quantum-Inspired Multidirectional Associative Memory. This is the first attempt to overcome these two problems effectively with robot application.
Quantum-inspired computing is an emerging research area, which has significantly improved the capabilities of conventional algorithms. In general, quantum-inspired hopfield associative memory (QHAM) has demonstrated quantum information processing in neural structures. This has resulted in an exponential increase in storage capacity while explaining the extensive memory, and it has the potential to illustrate the dynamics of neurons in the human brain when viewed from quantum mechanics perspective although the application of QHAM is limited as an autoassociation. We introduce a quantum-inspired multidirectional associative memory (QMAM) with a one-shot learning model, and QMAM with a self-convergent iterative learning model (IQMAM) based on QHAM in this paper. The self-convergent iterative learning enables the network to progressively develop a resonance state, from inputs to outputs. The simulation experiments demonstrate the advantages of QMAM and IQMAM, especially the stability to recall reliability.
There is increasing interest in quantum computing, because of its enormous computing potential. A small number of powerful quantum algorithms have been proposed to date; however, the development of new quantum algorithms for practical use remains essential. Parallel computing with a neural network has successfully realized certain unique functions such as learning and recognition; therefore, the introduction of certain neural computing techniques into quantum computing to enlarge the quantum computing application field is worthwhile. In this paper, a novel quantum associative memory (QuAM) is proposed, which is achieved with a quantum neural network by employing adiabatic Hamiltonian evolution. The memorization and retrieval procedures are inspired by the concept of associative memory realized with an artificial neural network. To study the detailed dynamics of our QuAM, we examine two types of Hamiltonians for pattern memorization. The first is a Hamiltonian having diagonal elements, which is known as an Ising Hamiltonian and which is similar to the cost function of a Hopfield network. The second is a Hamiltonian having non-diagonal elements, which is known as a neuro-inspired Hamiltonian and which is based on interactions between qubits. Numerical simulations indicate that the proposed methods for pattern memorization and retrieval work well with both types of Hamiltonians. Further, both Hamiltonians yield almost identical performance, although their retrieval properties differ. The QuAM exhibits new and unique features, such as a large memory capacity, which differs from a conventional neural associative memory.
The concept of fuzzy qubit is proposed as an approach to emulating quantum interference using fuzzy logic. This makes it possible to implement certain quantum computing algorithms, especially quantum associative memory, on hardware dedicated to fuzzy logic. The fuzzy qubit model is briefly reviewed and the fuzzy implementation of basic 1 and 2 qubit gates, such as Hadamard or C-not gates, is presented. The possibility of emulating quantum algorithms is discussed. Then quantum associative memory is presented and its implementation using the fuzzy qubit model is discussed. The advantages over the previous associative memory models, both quantum and classical, are discussed.
Using the effects of quantum mechanics for computing challenges has been an often discussed topic for decades. The frequent successes and early products in this area, which we have seen in recent years, indicate that we are currently entering a new era of computing. This paradigm shift will also impact the work of robotic scientists and the applications of robotics. New possibilities as well as new approaches to known problems will enable the creation of even more powerful and intelligent robots that make use of quantum computing cloud services or co-processors. In this position paper, we discuss potential application areas and also point out open research topics in quantum computing for robotics. We go into detail on the impact of quantum computing in artificial intelligence and machine learning, sensing and perception, kinematics as well as system diagnosis. For each topic we point out where quantum computing could be applied based on results from current research.
A novel quantum associative memory using the quantum holography scheme is presented. The patterns stored in the model are encoded into a set of independent quantum states. It overcomes the harm of nonorthogonal by storing these independent patterns into the memory using an iterative learning algorithm. The positive operation valued measurement is performed on the selectively recalled quantum state when does the information retrieval. Computer simulations show that the model exhibits good associative ability. Benefiting from the quantum implementation, the model not only can improve the performance and storage capacity, it also may be a suitable model to explain the biological associative phenomena because the phase of oscillation is inherent in the model.