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 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.
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.
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.
This article explores the groundbreaking intersection of quantum optics and neural network models, focusing on how multiphoton quantum interference in photonic circuits can simulate associative memory akin to the human brain. Drawing from recent research, we delve into the technical mechanisms, memory limits, and phase transitions observed in these systems. We employ geometric interpretations to elucidate the underlying ideas, simulate a model demonstrating latent memory in photon-like states for AI applications, and propose avenues for future research. The discussion covers historical context, experimental implications, energy efficiency advantages, potential challenges such as scalability and noise, and broader impacts on artificial intelligence, physics simulations, and neuromorphic hardware. Mamun, SM (2026)………………………1 Quantum Photonic Associative Memory: Multiphoton Interference as a Pathway to Brain-Inspired Computing (Vol-I) Also available on Academia.edu: https://www.academia.edu/attachments/132157923/download_file?s=portfolio
The increasing convergence of quantum computing (QC) and advanced robotic control is giving rise to a new class of intelligent automation systems. Utilizing quantum-inspired computational principles, contemporary robotic platforms demonstrate greater optimization efficiency, superior perceptual processing, and improved decision-making performance. This survey outlines the principal quantum-oriented control methodologies utilized in robotics, with particular emphasis on approaches that incorporate Quantum Neural Networks (QNN), Quantum Machine Learning (QML), and Quantum-behaved Particle Swarm Optimization (QPSO). The investigation systematically reviews recent advances in quantum-assisted adaptive control, trajectory planning, and optimization frameworks, spotlighting their contributions to improved robustness, precision, and energy efficiency. By analyzing research in quantum reinforcement learning, distributed Noisy Intermediate-Scale Quantum (NISQ) computing, and hybrid quantum-classical approaches, this review clarifies how quantum principles are transforming both theoretical and practical aspects of robotic control. Likewise, several real-world implementations ranging from industrial manipulators to autonomous and cognitive robotic systems demonstrate the growing feasibility of quantum-enhanced decision-making. The review concludes that while remarkable progress has been made in quantum-inspired control for robotics, most existing studies remain constrained to simulations with restricted experimental validation on physical platforms. Addressing this gap will be essential in future research to transition from theoretical and simulated models toward hardware-level implementations, enabling the realization of scalable, fault-tolerant quantum control architectures.
This book chapter explores the emerging field of quantum robotics and its potential to revolutionize the design and functionality of intelligent and adaptive robotic systems. It discusses the fundamental principles of quantum mechanics and their application to robotics, highlighting the advantages that quantum technologies offer in terms of computational power, information processing, and communication. The chapter presents various approaches to incorporating quantum elements into robotics, such as quantum sensors, quantum control systems, and quantum-inspired algorithms. Furthermore, it examines the challenges and opportunities in the development of quantum robotics, including hardware constraints, algorithm design, and the integration of classical and quantum components. Overall, this chapter provides an overview of the state-of-the-art in quantum robotics and identifies future directions for research and development.