The quantum enhanced stereo vision technique is thus a groundbreaking effort toward autonomous robotics, where autonomous robots have high precision 3D mapping through the application of quantum computational principles to solve the problem of classical stereo vision systems. However, real-time robotic navigation and object recognition is not allowed by such traditional methods due to depth estimation errors, noise, and occlusion issues. Based on that, this research offers a Quantum Assisted Adaptive Stereo Vision System (QASVS) that makes use of quantum superposition to enhance the depth estimation parallelism, entanglement for noise reduction, and a quantum optimized disparity computation model to increase the mapping accuracy. The system makes significant improvements in depth perception and computing speed at a real-time adaptive level, which could not be accomplished by the above-mentioned methods. To test the proposed framework, it is tested in a dynamic environment and found to be performing better in low light and complex terrains. These systems can be most crucial in autonomous drones, robotic surgical systems, industrial automation, and space exploration because precise depth perception is necessary. Processing speed, image quality, and environmental adaptability are improved by comparing to traditional stereo vision techniques. The study foresees quantum computing as a revolution for 3D vision for robotics that in turn will lead to the quantum enhanced machine perception. The next step will be building quantum photonic processors, as well as applying AI-driven depth refinement for more optimization.
The quantum enhanced stereo vision technique is thus a groundbreaking effort toward autonomous robotics, where autonomous robots have high precision 3D mapping through the application of quantum computational principles to solve the problem of classical stereo vision systems. However, real-time robotic navigation and object recognition is not allowed by such traditional methods due to depth estimation errors, noise, and occlusion issues. Based on that, this research offers a Quantum Assisted Adaptive Stereo Vision System (QASVS) that makes use of quantum superposition to enhance the depth estimation parallelism, entanglement for noise reduction, and a quantum optimized disparity computation model to increase the mapping accuracy. The system makes significant improvements in depth perception and computing speed at a real-time adaptive level, which could not be accomplished by the above-mentioned methods. To test the proposed framework, it is tested in a dynamic environment and found to be performing better in low light and complex terrains. These systems can be most crucial in autonomous drones, robotic surgical systems, industrial automation, and space exploration because precise depth perception is necessary. Processing speed, image quality, and environmental adaptability are improved by comparing to traditional stereo vision techniques. The study foresees quantum computing as a revolution for 3D vision for robotics that in turn will lead to the quantum enhanced machine perception. The next step will be building quantum photonic processors, as well as applying AI-driven depth refinement for more optimization.
The fusion of quantum computing and robotics represents a frontier in computational intelligence, promising to overcome the limitations of classical sensor fusion methods in precision, scalability, and real-time adaptability. This study proposes a hybrid quantum–classical framework designed to enhance the integration and synchronization of LiDAR, visual, and inertial sensor data, ultimately improving robotic perception and spatial awareness in complex environments. The model employs quantum-assisted optimization techniques to handle high-dimensional uncertainty, noise propagation, and data redundancy challenges inherent in multi-sensor processing. By leveraging variational quantum circuits and classical machine learning optimizers, the hybrid model achieves efficient data correlation and error minimization during sensor alignment. Benchmark experiments were conducted to evaluate the efficiency and precision of the proposed quantum-assisted sensor fusion system relative to conventional data integration algorithms. The findings reveal that hybrid quantum–classical systems yield substantial improvements in localization accuracy, temporal synchronization, and resilience to sensor noise, while maintaining computational feasibility within near-term quantum devices. This work highlights the potential of quantum-enhanced perception frameworks to accelerate the next generation of autonomous robotics, providing a foundation for adaptive control, intelligent navigation, and mission-critical decision-making under uncertainty.
Building a 3D scene from a limited number of images remains a challenging problem in computer vision, particularly for applications such as augmented reality, robotics, medical imaging, and autonomous navigation. Existing deep learning models like LRM and vFusion3D achieve high-quality reconstructions but require significant computational resources and long inference times, making them difficult to deploy on edge devices. This research introduces QuantumVision, a hybrid quantum-classical framework that integrates quantum image-processing techniques into a traditional 3D reconstruction pipeline. The framework consists of three modular components: quantum-assisted preprocessing, amplitude-based feature encoding, and a variational quantum optimization stage. These modules are designed to integrate with classical workflows while leveraging the potential advantages of Noisy Intermediate-Scale Quantum (NISQ) devices. Experimental simulations using Qiskit demonstrate improved feature extraction, edge detection, and reconstruction fidelity compared to classical baselines. The proposed approach highlights the potential of hybrid quantum computing to enhance computational efficiency and accuracy in partial-view 3D reconstruction tasks while remaining compatible with current quantum hardware limitations.
Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.
Quantum Machine Learning (QML) is a new direction within the investigation of presentday technologies that the developing need for accuracy in automated approaches has spurred.QML is changing the realm in optoelectronic robotic structures, according to this research.The present research objectives are to meet the growing demand for accuracy in dynamic optoelectronic environments across many industries by using quantum ideas to enhance choice-making precision.Some obstacles are specific to merging quantum computing with system learning, such as the complexity of algorithms and the constraints of quantum hardware.Adaptive Quantum Entanglement for Decision Fusion (AQE-DF) is a high-quality method that utilises adaptive quantum entanglement to facilitate effective choice fusion in optoelectronic robot systems.It is supplied on this paper as a groundbreaking method.Intending to enhance the robotic device's accuracy and flexibility, AQE-DF dynamically entangles quantum states linked to several preference routes.This lets in for the simultaneous assessment and integration of numerous preference possibilities.Multiple optoelectronic robot duties can be executed with AQE-DF, including complex manipulation, self-sufficient navigation, and real-time image processing.As this idea demonstrates, AQE-DF can convert the accuracy and flexibility of optoelectronic robotic structures by optimizing the desired fusion in those specific applications.A wonderful simulation study is completed to assess the practicability and efficiency of AQE-DF in numerous optoelectronic programs.It then shows convincing consequences, displaying that AQE-DF effectively improves choice-making precision, adaptability, and performance.
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.