Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Imaging and Applied Optics

Intelligent Quantum Sensing with Computational Neuromorphic Imaging

Chutian Wang, Madhav Gupta, Zhiqin Chu, Edmund Y. M. Lam

Published Jan 1, 2024• 0 citations
Publisher

Abstract

This work presents a solution that leverages the synergy of diamond quantum sensing and computational neuromorphic imaging, which brings high precision and a significant computation time reduction. It gives impetus to the advancement of more intelligent quantum sensing and computing capacity.
DOI
10.1364/cosi.2024.cm2b.1

Related papers

Widefield Diamond Quantum Sensing with Neuromorphic Vision Sensors (Adv. Sci. 2/2024)

Zhiyuan Du, Madhav Gupta, Feng Xu +9 more

Widefield Diamond Quantum Sensing In article number 2304355, Ngai Wong, Can Li, Zhiqin Chu, and co-workers employ a neuromorphic vision sensor to facilitate widefield diamond quantum sensing by converting variations in fluorescence intensity of diamond defects into spikes. This approach closely mimics the functionality of the human visual system, and thus significantly compresses data volume and minimizes latency. The present advancement offers novel perspectives for high-precision, low-latency quantum sensing, providing potential opportunities for integration with upcoming memory devices to develop intelligent quantum sensors.

Advanced Science•Jan 1, 2024•1 citation

Emerging Hybrid Models for Neuromorphic AI and Quantum Computing

Advances in computational intelligence and robotics book series•Mar 4, 2026•1 citation

Quantum–Neuromorphic–Edge AI Architectures for Next-Generation Intelligence

Piyush raj

•Jan 1, 2026•0 citations

The Convergence of Quantum Computing and Artificial Intelligence

Nitu Tank

This chapter discusses the intersection of quantum computing, artificial intelligence and neuromorphic engineering as a basis of the next generation hybrid intelligent systems. It describes the role of quantum processing in providing high-dimensional reasoning and probabilistic optimization, and the ability of neuromorphic hardware to do spiking computations at real time and using less energy. These paradigms and their combination address the scalability and performance constraints of classical AI. The chapter examines major hybrid architectures, neuromorphic-to-quantum encoding algorithms, quantum-assisted learning algorithms, and integrated inference pipelines. Another significant aspect that it brings to the fore is the key areas of application, including robotics, healthcare, finance, and edge intelligence, as well as engineering limitations, moral implications, and safety issues of AGI. An outlook research road identifies essential milestones towards scalable, secure, and interpretable hybrid neuromorphic quantum systems.

Advances in computational intelligence and robotics book series•Mar 4, 2026•0 citations

Quantum Neuromorphic Intelligence Analysis Geopolitical Forecasting and Military Strategy

Alapeti Ware

Quantum Neuromorphic Intelligence Analysis (QNIA) represents the convergence of quantum computing and neuromorphic computing to revolutionize predictive analytics in complex geopolitical events, terrorism threat assessment, and warfare strategy formulation. QNIA leverages the exponential computational capabilities of quantum computing employing algorithms such as Grover's and Shor's to perform rapid, high-dimensional data analysis while concurrently harnessing the adaptive, energy-efficient processing of neuromorphic architectures that mimic biological neural networks. This synergistic integration facilitates the development of advanced quantum-assisted deep learning models capable of detecting subtle anomalies and forecasting conflicts with unprecedented accuracy. This paper investigates the potential of QNIA to transform intelligence gathering and military decision-making processes in real time. It details how quantum-enhanced machine learning algorithms, when combined with neuromorphic processors, can process vast streams of global intelligence data, enabling the prediction of diplomatic tensions, economic warfare indicators, and cyber-attack patterns. By incorporating probabilistic models, QNIA quantitatively assesses QNIA A Preprint threat levels, optimizes resource allocation, and supports the formulation of proactive military strategies. Moreover, the study explores the ethical and strategic implications of deploying such integrated systems in high-stakes environments, emphasizing both their potential to enhance security measures and the risks associated with autonomous decision-making. By synthesizing theoretical frameworks from quantum mechanics, neuromorphic engineering, and artificial intelligence and incorporating insights from quantum machine learning (Biamonte et al., 2017) and recent evaluations of brain-inspired computing (Mehonic & Kenyon, 2020) this research provides a comprehensive overview of QNIA's capabilities and its transformative impact on modern defense and intelligence strategies. The convergence of these emerging technologies signifies a pivotal shift toward more agile, data-driven, and anticipatory security solutions, setting the stage for a new era in predictive geopolitical analysis and military strategy.

•Feb 19, 2025•0 citations

Neuromorphic computing with single-element quantum reservoirs

Luke C. G. Govia, William D. Kalfus, Guilhem J. Ribeill +3 more

Bulletin of the American Physical Society•Mar 18, 2021•0 citations
Widefield Diamond Quantum Sensing In article number 2304355, Ngai Wong, Can Li, Zhiqin Chu, and co-workers employ a neuromorphic vision sensor to facilitate widefield diamond quantum sensing by converting variations in fluorescence intensity of diamond defects into spikes. This approach closely mimics the functionality of the human visual system, and thus significantly compresses data volume and minimizes latency. The present advancement offers novel perspectives for high-precision, low-latency quantum sensing, providing potential opportunities for integration with upcoming memory devices to develop intelligent quantum sensors.
This chapter discusses the intersection of quantum computing, artificial intelligence and neuromorphic engineering as a basis of the next generation hybrid intelligent systems. It describes the role of quantum processing in providing high-dimensional reasoning and probabilistic optimization, and the ability of neuromorphic hardware to do spiking computations at real time and using less energy. These paradigms and their combination address the scalability and performance constraints of classical AI. The chapter examines major hybrid architectures, neuromorphic-to-quantum encoding algorithms, quantum-assisted learning algorithms, and integrated inference pipelines. Another significant aspect that it brings to the fore is the key areas of application, including robotics, healthcare, finance, and edge intelligence, as well as engineering limitations, moral implications, and safety issues of AGI. An outlook research road identifies essential milestones towards scalable, secure, and interpretable hybrid neuromorphic quantum systems.
Quantum Neuromorphic Intelligence Analysis (QNIA) represents the convergence of quantum computing and neuromorphic computing to revolutionize predictive analytics in complex geopolitical events, terrorism threat assessment, and warfare strategy formulation. QNIA leverages the exponential computational capabilities of quantum computing employing algorithms such as Grover's and Shor's to perform rapid, high-dimensional data analysis while concurrently harnessing the adaptive, energy-efficient processing of neuromorphic architectures that mimic biological neural networks. This synergistic integration facilitates the development of advanced quantum-assisted deep learning models capable of detecting subtle anomalies and forecasting conflicts with unprecedented accuracy. This paper investigates the potential of QNIA to transform intelligence gathering and military decision-making processes in real time. It details how quantum-enhanced machine learning algorithms, when combined with neuromorphic processors, can process vast streams of global intelligence data, enabling the prediction of diplomatic tensions, economic warfare indicators, and cyber-attack patterns. By incorporating probabilistic models, QNIA quantitatively assesses QNIA A Preprint threat levels, optimizes resource allocation, and supports the formulation of proactive military strategies. Moreover, the study explores the ethical and strategic implications of deploying such integrated systems in high-stakes environments, emphasizing both their potential to enhance security measures and the risks associated with autonomous decision-making. By synthesizing theoretical frameworks from quantum mechanics, neuromorphic engineering, and artificial intelligence and incorporating insights from quantum machine learning (Biamonte et al., 2017) and recent evaluations of brain-inspired computing (Mehonic & Kenyon, 2020) this research provides a comprehensive overview of QNIA's capabilities and its transformative impact on modern defense and intelligence strategies. The convergence of these emerging technologies signifies a pivotal shift toward more agile, data-driven, and anticipatory security solutions, setting the stage for a new era in predictive geopolitical analysis and military strategy.