In recent years, researchers have paid more attention to the hybrid applications of quantum computing and brain-computer interfaces. With the development of neural technology and artificial intelligence, scientists have become more and more researching brain-computer interface, and the application of brain-computer interface technology to more fields has gradually become the focus of research. While the field of brain-computer interface has evolved rapidly over the past decades, the core technologies and innovative ideas behind seemingly unrelated brain-computer interface systems are rarely summarized from the point of integration with quantum. This paper provides a detailed report on the hybrid applications of quantum computing and brain-computer interface, indicates the current problems, and gives suggestions on the hybrid application research direction.
In recent years, researchers have paid more attention to the hybrid applications of quantum computing and brain-computer interfaces. With the development of neural technology and artificial intelligence, scientists have become more and more researching brain-computer interface, and the application of brain-computer interface technology to more fields has gradually become the focus of research. While the field of brain-computer interface has evolved rapidly over the past decades, the core technologies and innovative ideas behind seemingly unrelated brain-computer interface systems are rarely summarized from the point of integration with quantum. This paper provides a detailed report on the hybrid applications of quantum computing and brain-computer interface, indicates the current problems, and gives suggestions on the hybrid application research direction.
This research explores the potential of quantum computing to revolutionize brain-computer interfaces (BCIs) by providing a fundamentally different approach to simulating neuronal activity and information transfer. The core claim posits that quantum algorithms can accurately model the intricate quantum effects within neural networks, leading to more precise brain interface design and therapeutic interventions. This study will detail the challenges and opportunities inherent in harnessing quantum mechanics for BCI applications, focusing on the unique capabilities of quantum bits (qubits) and their potential to overcome limitations of classical computing. We will examine current research directions, including the development of specialized quantum algorithms for neural network modeling, and discuss the technical and theoretical hurdles that must be addressed before practical implementation becomes feasible.
Abstract Brain–computer interfaces (BCIs) can revolutionize how humans interact with technology, but several scientific and technological challenges must be addressed to realize their full potential. Recent developments in quantum‐based sensing methods offer promising solutions to some of these challenges. This review provides an overview of the progress, challenges, and prospects of BCIs research and discuss the feasibility of integrating quantum sensor technology in BCI systems. The applications of quantum sensing in BCIs research are reviewed and the solution based on quantum sensor technology to overcome some of the challenges associated with BCI systems is proposed. The potential of quantum sensor technology for the future development of BCIs is emphasized. Overall, this review highlights quantum sensor technology's significant potential for future development of BCI.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
In this paper, we overview basic quantum machine learning algorithms and we highlight their potential application for rehabilitation brain computer interface systems. The paper provides a very brief introduction to basic quantum theory issues, such as quantum registers and quantum gates, as well as basic limitations of brain computer interface systems. Finally, we present references, ideas and research guidelines on how quantum theory can be used to deal with such limitations. Our ideas might result in "brain quantum-computer interfaces", as soon as quantum computers become widely available, resulting in real-time apparatus.
In recent years, quantum computing has emerged as a potentially gamechanging technology, with applications across various disciplines, including AI and machine learning.In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs).This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks.This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks.Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs.Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.
We propose Quantum Brain Networks (QBraiNs) as a new interdisciplinary field integrating knowledge and methods from neurotechnology, artificial intelligence, and quantum computing. The objective is to develop an enhanced connectivity between the human brain and quantum computers for a variety of disruptive applications. We foresee the emergence of hybrid classical-quantum networks of wetware and hardware nodes, mediated by machine learning techniques and brain–machine interfaces. QBraiNs will harness and transform in unprecedented ways arts, science, technologies, and entrepreneurship, in particular activities related to medicine, Internet of Humans, intelligent devices, sensorial experience, gaming, Internet of Things, crypto trading, and business.